In this tutorial, you will learn to handle a complete state-of-the-art HMM-based speech recognition system. The system you will use is the SPHINX system, designed at Carnegie Mellon University. SPHINX is one of the best and most versatile recognition systems in the world today.
An HMM-based system, like all other speech recognition systems, functions by first learning the characteristics (or parameters) of a set of sound units, and then using what it has learned about the units to find the most probable sequence of sound units for a given speech signal. The process of learning about the sound units is called training. The process of using the knowledge acquired to deduce the most probable sequence of units in a given signal is called decoding, or simply recognition.
Accordingly, you will need those components of the SPHINX system that you can use for training and for recognition. In other words, you will need the SPHINX trainer and a SPHINX decoder.
You will be given instructions on how to download, compile, and run the components needed to build a complete speech recognition system. Namely, you will be given instructions on how to use SphinxTrain and SPHINX-3. Please check a CMUSphinx project page for more details on available decoders and their applications. This tutorial does not instruct you on how to build a language model, but you can check the CMU SLM Toolkit page for an excellent manual.
At the end of this tutorial, you will be in a position to train and use this system for your own recognition tasks. More importantly, through your exposure to this system, you will have learned about several important issues involved in using a real HMM-based ASR system.
Important note for members of the Sphinx group: This tutorial now supports the PBS queue. The internal, csh-based Robust tutorial is still available, though its use is discouraged.
The SPHINX trainer consists of a set of programs, each responsible for a well defined task, and a set of scripts that organizes the order in which the programs are called. You have to compile the code in your favorite platform.
The trainer learns the parameters of the models of the sound units using a set of sample speech signals. This is called a training database. A choice of training databases will also be provided to you. The trainer also needs to be told which sound units you want it to learn the parameters of, and at least the sequence in which they occur in every speech signal in your training database. This information is provided to the trainer through a file called the transcript file, in which the sequence of words and non-speech sounds are written exactly as they occurred in a speech signal, followed by a tag which can be used to associate this sequence with the corresponding speech signal. The trainer then looks into a dictionary which maps every word to a sequence of sound units, to derive the sequence of sound units associated with each signal. Thus, in addition to the speech signals, you will also be given a set of transcripts for the database (in a single file) and two dictionaries, one in which legitimate words in the language are mapped sequences of sound units (or sub-word units), and another in which non-speech sounds are mapped to corresponding non-speech or speech-like sound units. We will refer to the former as the language dictionary and the latter as the filler dictionary.
The decoder also consists of a set of programs, which have been compiled to give a single executable that will perform the recognition task, given the right inputs. The inputs that need to be given are: the trained acoustic models, a model index file, a language model, a language dictionary, a filler dictionary, and the set of acoustic signals that need to be recognized. The data to be recognized are commonly referred to as test data.
In addition to these components, you will need the acoustic models that you have trained for recognition. You will have to provide these to the decoder. While you train the acoustic models, the trainer will generate appropriately named model-index files. A model-index file simply contains numerical identifiers for each state of each HMM, which are used by the trainer and the decoder to access the correct sets of parameters for those HMM states. With any given set of acoustic models, the corresponding model-index file must be used for decoding. If you would like to know more about the structure of the model-index file, you will find a description following the link Creating the CI model definition file.
You will have to download and build several components to set up the complete systems. Provided you have all the necessary software, you will have to download the data package, the trainer, and one of the SPHINX decoders. The following instructions detail the steps.
You will need Perl to run the provided scripts, and a C compiler to compile the source code.
You will need Perl to use the scripts provided. Linux usually comes with some version of Perl. If you do not have Perl installed, please check the Perl site, where you can download it for free.
SphinxTrain and SPHINX-3 use GNU autoconf to find out basic information about your system, and should compile on most Unix and Unix-like systems, and certainly on Linux. The code compiles using GNU's make and GNU's C compiler (gcc), available in all Linux distributions, and available for free for most platforms.
We also provide files supporting compilation using Microsoft's Visual C++, i.e., the solution (.sln) and project (.vcproj) files needed to compile code in native Windows format.
You will need a word alignment program if you want to measure the accuracy of a decoder. A commonly used one, available from the National Institute of Standards and Technology (NIST), is sclite, provided as part of their scoring packages. You will find their scoring packages in the NIST tools page. The software is available for those in the speech group at ~robust/archive/third_party_packages/NIST_scoring_tools/sctk/linux/bin/sclite.
Internally, at CMU, you may also want to use the align program, which does the same job as the NIST program, but does not have some of the features. You can find it in the robust home directory at ~robust/archive/third_party_packages/align/linux/align.
The Sphinx Group makes it available two audio databases that can be used with this tutorial. Each has its peculiarities, and are provided just as a convenience. The data provided are not sufficient to build a high performance speech recognition system. They are only provided with the goal of helping you learn how to use the system.
The databases are provided at the Databases page. Choose either AN4 or RM1. AN4 includes the audio, but it is a very small database. You can choose it if you want to include the creation of feature files in your experiments. RM1 is a little larger, thus resulting in a system with slightly better performance. Audio is not provided, since it is licensed material. We provide the feature files used directly by the trainer and decoders. For more information about RM1, please check with the LDC.
Download the audio tarball, either AN4 or RM1, by clicking on the link and choosing "Save" when the dialog window appears. Save it to the same tutorial directory you just created. For those not familiar with the term, a tarball in our context is a file with extension .tar.gz. Extract the contents as follows.
In Windows, using the Windows Explorer, go to the tutorial directory, right-click the audio tarball, and choose "Extract to here" in the WinZip menu.
SphinxTrain can be retrieved using subversion (svn) or by downloading a tarball. svn makes it easier to update the code as new changes are added to the repository, but requires you to install svn. The tarball is more readily available.
You can find more information about svn at the SVN Home.
Using the tarball, download the SphinxTrain tarball by clicking on the link and choosing "Save" when the dialog window appears. Save it to the same tutorial directory. Extract the contents as follows.
In Windows, using the Windows Explorer, go to the tutorial directory, right-click the SphinxTrain tarball, and choose "Extract to here" in the WinZip menu.
Double click the file tutorial/SphinxTrain/SphinxTrain.sln. This will open MS Visual C++, if you have it installed. If you do not, please contact Microsoft.
In the Menu Build choose Batch Build, and select all items. Click on Rebuild All This will build all executables needed by the trainer.
After compiling the code, you will have to setup the tutorial by copying all relevant executables and scripts to the same area as the data. Assuming your current working directory is tutorial, you will need to do the following.
The Sphinx Group has several different decoders whose features can guide you in choosing the best one for your application. Roughly, these can be described as follows.
PocketSphinx: This is a modernized version of Sphinx-2, specially optimized for embedded and handheld systems. It also consumes on average 20% less memory and 5-20% less CPU time than SPHINX-2. However, it is under active development, so the interface and feature set may be unstable.
SPHINX-3: Uses continuous HMMs. It can handle both live and batch decoding. Currently, it is the decoder most actively developed.
SPHINX-4: Uses continuous HMMs. It was written in the Java programming language. It provides high flexibility and great accuracy and speed for small tasks.
For your application you can choose any decoder suitable for you, but in this tutorial we'll use SPHINX-3 as a base decoder. It's a good idea to test your model with SPHINX-3 first to detect errors on early stages.
SPHINX-3 can be retrieved using subversion (svn) or by downloading a tarball. svn makes it easier to update the code as new changes are added to the repository, but requires you to install svn. The tarball is more readily available. SPHINX-3 is also available as a release from SourceForge.net. Since the release is a tarball, we will not provide separate instructions for installation of the release.
Using the tarball, download the sphinx3 tarball and sphinxbase by clicking on the link and choosing "Save" when the dialog window appears. Save them to the same tutorial directory. Extract the contents as follows.
In Windows, using the Windows Explorer, go to the tutorial directory, right-click the sphinxbase and sphinx3 tarballs, and choose "Extract to here" in the WinZip menu.
# here. If you downloaded the tarball, you do not need to run it.
Double click the file tutorial/sphinxbase/sphinxbase.sln. This will open MS Visual C++, if you have it installed. If you do not, please contact Microsoft.
In the Menu Build choose Batch Build, and select all items. Click on Rebuild All This will build all libraries in the SphinxBase package.
Double click the file tutorial/sphinx3/programs.sln. This will open MS Visual C++, if you have it installed. If you do not, please contact Microsoft.
In the Menu Build choose Batch Build, and select all items. Click on Rebuild All This will build all executables in the SPHINX-3 package.
The scripts should work "out of the box", unless you are training models for PocketSphinx. In this case, you have to edit the file etc/sphinx_train.cfg, uncommenting the line defining the variable $CFG_HMM_TYPE so that it looks like the box below.
The system does not directly work with acoustic signals. The signals are first transformed into a sequence of feature vectors, which are used in place of the actual acoustic signals. To perform this transformation (or parameterization) from within the directory an4, type the following command on the command line. If you are using Windows instead of linux, please replace the / character with \. Notice that if you downloaded rm1 instead, the files are already provided in cepstra format, so you do not need, and in fact, cannot, follow this step.
This script will compute, for each training utterance, a sequence of 13-dimensional vectors (feature vectors) consisting of the Mel-frequency cepstral coefficients (MFCCs). Note that the list of wave files contains a list with the full paths to the audio files. Since the data are all located in the same directory as you are working, the paths are relative, not absolute. You may have to change this, as well as the an4_test.fileids file, if the location of data is different. This step takes approximately 10 minutes to complete on a fast machine, but time may vary. As it is running, you might want to continuing reading. The MFCCs will be placed automatically in a directory called ./feat. Note that the type of features vectors you compute from the speech signals for training and recognition, outside of this tutorial, is not restricted to MFCCs. You could use any reasonable parameterization technique instead, and compute features other than MFCCs. SPHINX-3 and SPHINX-4 can use features of any type or dimensionality. In this tutorial, however, you will use MFCCs for two reasons: a) they are currently known to result in the best recognition performance in HMM-based systems under most acoustic conditions, and b) this tutorial is not intended to cover the signal processing aspects of speech parameterization and only aims for a standard usable platform in this respect. Now you can begin to train the system.
In the scripts directory (./scripts_pl), there are several directories numbered sequentially from 00* through 99*. Each directory either has a directory named slave*.pl or it has a single file with extension .pl. Sequentially go through the directories and execute either the the slave*.pl or the single .pl file, as below. As usual, if you are using Windows instead of linux, you have to replace the / character with \.
Alternatively, you can simply run the RunAll.pl script provided.
From here on, we will refer to the script that you have to run in each directory as simply slave*.pl. In directories where no such a file exists, please understand it as the single .pl file present in that directory.
The scripts will launch jobs on your machine, and the jobs will take a few minutes each to run through. Before you run any script, note the directory contents of your current directory. After you run each slave*.pl note the contents again. Several new directories will have been created. These directories contain files which are being generated in the course of your training. At this point you need not know about the contents of these directories, though some of the directory names may be self explanatory and you may explore them if you are curious.
One of the files that appears in your current directory is an .html file, named an4.html or rm1.html, depending on which database you are using. This file will contain a status report of jobs already executed. Verify that the job you launched completed successfully. Only then launch the next slave*.pl in the specified sequence. Repeat this process until you have run the slave*.pl in all directories.
Note that in the process of going through the scripts in 00* through 99*, you will have generated several sets of acoustic models, each of which could be used for recognition. Notice also that some of the steps are required only for the creation of semi-continuous models. If you execute these steps while creating continuous models, the scripts will benignly do nothing. Once the jobs launched from 20.ci_hmm have run to completion, you will have trained the Context-Independent (CI) models for the sub-word units in your dictionary. When the jobs launched from the 30.cd_hmm_untied directory run to completion, you will have trained the models for Context-Dependent sub-word units (triphones) with untied states. These are called CD-untied models and are necessary for building decision trees in order to tie states. The jobs in 40.buildtrees will build decision trees for each state of each sub-word unit. The jobs in 45.prunetree will prune the decision trees and tie the states. Following this, the jobs in 50.cd-hmm_tied will train the final models for the triphones in your training corpus. These are called CD-tied models. The CD-tied models are trained in many stages. We begin with 1 Gaussian per state HMMs, following which we train 2 Gaussian per state HMMs and so on till the desired number of Gaussians per State have been trained. The jobs in 50.cd-hmm_tied will automatically train all these intermediate CD-tied models. At the end of any stage you may use the models for recognition. Remember that you may decode even while the training is in progress, provided you are certain that you have crossed the stage which generates the models you want to decode with. Before you decode, however, read the section called How to decode, and key decoding issues to learn a little more about decoding. This section also provides all the commands needed for decoding with each of these models.
You have now completed your training. The final models and location will depend on the database and the model type that you are using. If you are using RM1 to train continuous models, you will find the parameters of the final 8 Gaussian/state 3-state CD-tied acoustic models (HMMs) with 1000 tied states in a directory called ./model_parameters/rm1.cd_cont_1000_8/. You will also find a model-index file for these models called rm1.1000.mdef in ./model_architecture/ . This file, as mentioned before, is used by the system to associate the appropriate set of HMM parameters with the HMM for each sound unit you are modeling. The training process will be explained in greater detail later in this document. If, however, you trained semi-continuous models with AN4, the final models will be located at ./model_parameters/an4.1000.s2models, where you will find all files need to decode with pocketsphinx.
This step had 6 ERROR messages and 2 WARNING messages. Please check the log file for details.
Don't care about them, it's perfectly fine to have several alignment errors in a database. The main sources of errors are small amount of data like in an4 and the bad quality of recordings. This cause "final state not reached" errors in the log or "mgau less then zero" errors. If there are too many errors, it means something goes wrong though and it's time to check the training setup.
This will take approximately 10 minutes to run.
You are now ready to decode. Type the command below.
This uses all of the components provided to you for decoding, including the acoustic models and model-index file that you have generated in your preliminary training run, to perform recognition on your test data. When the recognition job is complete, the script computes the recognition Word Error Rate (WER) or Sentence Error Rate (SER). Notice that the script comes with a very simple built-in function that computes the SER. Unless you are using CMU machines, if you want to compute the WER you will have to download and compile code to do so. A popular one, used as a standard in the research community, is available from NIST. Check the section on Word Alignment.
The second percentage number (9.470%) is the WER and has been obtained using the 8 Gaussians per state HMMs that you have just trained in the preliminary training run. Other numbers in the above output will be explained later in this document. The WER may vary depending on which decoder you used.
Notice that the reported error rates refer to word error rate (WER) in the first case, and sentence error rate (SER) in the second, so you can expect them to be wildly different.
Three tools are provided that can help you find problems with your setup. You will find two of these executables in the directory bin. You can download and install the third as indicated below.
mk_mdef_gen: Phone and triphone frequency analysis tool. You can use this to count the relative frequencies of occurrence of your basic sound units (phones and triphones) in the training database. Since HMMs are statistical models, what you are aiming for is to design your basic units such that they occur frequently enough for their models to be well estimated, while maintaining enough information to minimize confusions between words. This issue is explained in greater detail in Appendix 1.
printp: Tool for viewing the model parameters being estimated.
You are expected to train the SPHINX system using all the components provided for training. The trainer will generate a set of acoustic models. You are expected to use these acoustic models and the rest of the decoder components to recognize what has been said in the test data set. You are expected to compare your recognition output to the "correct" sequence of words that have been spoken in the test data set (these will also be given to you), and find out the percentage of errors you made (the word error rate, WER, or the sentence error rate, SER).
Q. What is your word or sentence error rate, what did you do to achieve it, and why?
A satisfactory answer to this question would comprise of any well thought out and justified manipulation of any training file(s) or parameter(s). Remember that speech recognition is a complex engineering problem and that you are not expected to be able to manipulate all aspects of the system in a single tutorial session.
You are now ready to begin your own exercises. For every training and decoding run, you will need to first give it a name. We will refer to the experiment name of your choice by $taskname. For example, the names given to the experiments using the two available databases are an4, and rm1. Your choice of $taskname will be used automatically in all the files for that training and recognition run for easy identification. All directories and files needed for this experiment will be copied to a directory named $taskname. Some of these files, such as data, will be provided by you (maybe copied from either tutorial/an4 or tutorial/rm1). Other files will be automatically copied from the trainer or decoder installations.
A new task is created from an existing one in a directory named $taskname in parallel to the existing one. Assuming that you are copying a setup from the existing setup named tutorial/an4, the new task will be located at tutorial/$taskname. Remember to replace $taskname with the name of your choice.
In the following example, we do just that: we copy a setup from the an4 setup. Notice that your current working directory is the existing setup. The new one will be created by the script.
This will create a new setup by rerunning the SphinxTrain setup, then rerunning the decoder setup using the same decoder as used by the originating setup (in this case, an4), and then copying the configuration files, located under etc, to the new setup, with the file names matching the new task's.
Be warned that the copy_setup.pl script also copies the data, located under feat and wav, to the new location. If your dataset is large, this duplication may be wasting disk space. A great option would be to just link the data directories. The script, as is, does not support this because not all operating systems can create symbolic links.
After this you will work entirely within this $taskname directory.
Your tutorial exercise begins with training the system using the MFCC feature files that you have already computed during your preliminary run. However, when you train this time, you will be required to take certain decisions based on what you know and the information that is provided to you in this document. The decisions that you take will affect the quality of the models that you train, and thereby the recognition performance of the system.
You must now go through the following steps in sequence.
Parameterize the training database, if you used the an4 database or are using your own data. If you used an4, you have already done this for every training utterance during your preliminary run. If you used rm1, the data were provided already parameterized. At this point you do not have to do anything further except to note that in the speech recognition field it is common practice to call each file in a database an "utterance". The signal in an "utterance" may not necessarily be a full sentence. You can view the cepstra in any file by using the tool cepview.
Decide what sound units you are going to ask the system to train. To do this, look at the language dictionary $taskname/etc/$taskname.dic and the filler dictionary $taskname/etc/$taskname.filler, and note the sound units in these. A list of all sound units in these dictionaries is also written in the file $taskname/etc/$taskname.phone. Study the dictionaries and decide if the sound units are adequate for recognition. In order to be able to perform good recognition, sound units must not be confusable, and must be consistently used in the dictionary. Look at Appendix 1 for an explanation.
Also check whether these units, and the triphones they can form (for which you will be building models ultimately), are well represented in the training data. It is important that the sound units being modeled be well represented in the training data in order to estimate the statistical parameters of their HMMs reliably. To study their occurrence frequencies in the data, you may use the tool mk_mdef_gen. Based on your study, see if you can come up with a better set of sound units to train.
You can restructure the set of sound units given in the dictionaries by merging or splitting existing sound units in them. By merging of sound units we mean the clustering of two or more different sound units into a single entity. For example, you may want to model the sounds "Z" and "S" as a single unit (instead of maintaining them as separate units). To merge these units, which are represented by the symbols Z and S in the language dictionary given, simply replace all instances of Z and S in the dictionary by a common symbol (which could be Z_S, or an entirely new symbol). By splitting of sound units we mean the introduction of multiple new sound units in place of a single sound unit. This is the inverse process of merging. For example, if you found a language dictionary where all instances of the sounds Z and S were represented by the same symbol, you might want to replace this symbol by Z for some words and S for others. Sound units can also be restructured by grouping specific sequences of sound into a single sound. For example, you could change all instances of the sequence "IX D" into a single sound IX_D. This would introduce a new symbol in the dictionary while maintaining all previously existing ones. The number of sound units is effectively increased by one in this case. There are other techniques used for redefining sound units for a given task. If you can think of any other way of redefining dictionaries or sound units that you can properly justify, we encourage you to try it.
Once you re-design your units, alter the file $taskname/etc/$taskname.phone accordingly. Make sure you do not have spurious empty spaces or lines in this file.
Alternatively, you may bypass this design procedure and use the phone list and dictionaries as they have been provided to you. You will have occasion to change other things in the training later.
Once you have fixed your dictionaries and the phone list file, edit the file etc/sphinx_train.cfg in tutorial/$taskname/ to change the following training parameters.
$CFG_STATESPERHMM = it could be any integer, but we recommend 3 or 5. The number of states in an HMMs is related to the time-varying characteristics of the sound units. Sound units which are highly time-varying need more states to represent them. The time-varying nature of the sounds is also partly captured by the $CFG_SKIPSTATE variable that is described below.
$CFG_SKIPSTATE =set this to no or yes. This variable controls the topology of your HMMs. When set to yes, it allows the HMMs to skip states. However, note that the HMM topology used in this system is a strict left-to-right Bakis topology. If you set this variable to no, any given state can only transition to the next state. In all cases, self transitions are allowed. See the figures in Appendix 2 for further reference. You will find the HMM topology file, conveniently named $taskname.topology, in the directory called model_architecture/ in your current base directory ($taskname).
$CFG_FINAL_NUM_DENSITIES = if you are training semi-continuous models, set this number, as well as $CFG_INITIAL_NUM_DENSITIES, to 256. For continuous, set $CFG_INITIAL_NUM_DENSITIES to 1 and $CFG_FINAL_NUM_DENSITIES to any number from 1 to 8. Going beyond 8 is not advised because of the small training data set you have been provided with. The distribution of each state of each HMM is modeled by a mixture of Gaussians. This variable determines the number of Gaussians in this mixture. The number of HMM parameters to be estimated increases as the number of Gaussians in the mixture increases. Therefore, increasing the value of this variable may result in less data being available to estimate the parameters of every Gaussian. However, increasing its value also results in finer models, which can lead to better recognition. Therefore, it is necessary at this point to think judiciously about the value of this variable, keeping both these issues in mind. Remember that it is possible to overcome data insufficiency problems by sharing the Gaussian mixtures amongst many HMM states. When multiple HMM states share the same Gaussian mixture, they are said to be shared or tied. These shared states are called tied states (also referred to as senones). The number of mixtures you train will ultimately be exactly equal to the number of tied states you specify, which in turn can be controlled by the $CFG_N_TIED_STATES parameter described below.
$CFG_N_TIED_STATES = set this number to any value between 500 and 2500. This variable allows you to specify the total number of shared state distributions in your final set of trained HMMs (your acoustic models). States are shared to overcome problems of data insufficiency for any state of any HMM. The sharing is done in such a way as to preserve the "individuality" of each HMM, in that only the states with the most similar distributions are tied. The $CFG_N_TIED_STATES parameter controls the degree of tying. If it is small, a larger number of possibly dissimilar states may be tied, causing reduction in recognition performance. On the other hand, if this parameter is too large, there may be insufficient data to learn the parameters of the Gaussian mixtures for all tied states. (An explanation of state tying is provided in Appendix 3). If you are curious, you can see which states the system has tied for you by looking at the ASCII file $taskname/model_architecture/$taskname.$CFG_N_TIED_STATES.mdef and comparing it with the file $taskname/model_architecture/$taskname.untied.mdef. These files list the phones and triphones for which you are training models, and assign numerical identifiers to each state of their HMMs.
$CFG_CONVERGENCE_RATIO = set this to a number between 0.1 to 0.001. This number is the ratio of the difference in likelihood between the current and the previous iteration of Baum-Welch to the total likelihood in the previous iteration. Note here that the rate of convergence is dependent on several factors such as initialization, the total number of parameters being estimated, the total amount of training data, and the inherent variability in the characteristics of the training data. The more iterations of Baum-Welch you run, the better you will learn the distributions of your data. However, the minor changes that are obtained at higher iterations of the Baum-Welch algorithm may not affect the performance of the system. Keeping this in mind, decide on how many iterations you want your Baum-Welch training to run in each stage. This is a subjective decision which has to be made based on the first convergence ratio which you will find written at the end of the log file for the second iteration of your Baum-Welch training ($taskname/logdir/0*/$taskname.*.2.norm.log. Usually, 5-15 iterations are enough, depending on the amount of data you have. Do not train beyond 15 iterations. Since the amount of training data is not large you will over-train the models to the training data.
$CFG_NITER = set this to an integer number between 5 to 15. This limits the number of iterations of Baum-Welch to the value of $CFG_NITER.
Once you have made all the changes desired, you must train a new set of models. You can accomplish this by re-running all the slave*.pl scripts from the directories $taskname/scripts_pl/00* through $taskname/scripts_pl/09*, or simply by running perl scripts_pl/RunAll.pl.
The first step in decoding is to compute the MFCC features for your test utterances. Since you have already done this in the preliminary run, you do not have to repeat the process here.
You may change decoder parameters, affecting the recognition results, by editing the file etc/sphinx_decode.cfg in tutorial/$taskname/. Some of the interesting parameters follow.
$DEC_CFG_DICTIONARY = the dictionary used by the decoder. It may or may not be the same as the one used for training. The set of phones has be be contained in the set of phones from the trainer dictionary. The set of words can be larger. Normally, though, the decoder dictionary is the same as the trainer one, especially for small databases.
$DEC_CFG_FILLERDICT = the filler dictionary.
$DEC_CFG_GAUSSIANS = the number of densities in the model used by the decoder. If you trained continuous models, the process of training creates intermediate models where the number of Gaussians is 1, 2, 4, 8, etc, up to the total number you chose. You can use any of those in the decoder. In fact, you are encouraged to do so, so you get a sense of how this affects the recognition accuracy. You are encouraged to find the best number of densities for databases with different complexities.
$DEC_CFG_MODEL_NAME = the model name. It defaults to using the context dependent (CD) tied state models with the number of senones and number of densities specified in the training step. You are encouraged to also use the CD untied and also the context independent (CI) models to get a sense to how accuracy changes.
$DEC_CFG_LANGUAGEWEIGHT the language weight. A value between 6 and 13 is recommended. The default depends on the database that you downloaded. The language model and the language weight are described in Appendix 4. Remember that the language weight decides how much relative importance you will give to the actual acoustic probabilities of the words in the hypothesis. A low language weight gives more leeway for words with high acoustic probabilities to be hypothesized, at the risk of hypothesizing spurious words.
$DEC_CFG_ALIGN = the path to the program that performs word alignment, or builtin, if you do not have one.
You may decode several times with changing the variables above without re-training the acoustic models, to decide what is best for you.
The script scripts_pl/decode/slave.pl already computes the word or sentence accuracy when it finishes decoding. It will add a line to the top level .html page that looks like the following if you are using NIST's sclite.
In this line the first percentage indicates the percentage of words in the test set that were correctly recognized. However, this is not a sufficient metric - it is possible to correctly hypothesize all the words in the test utterances merely by hypothesizing a large number of words for each word in the test set. The spurious words, called insertions, must also be penalized when measuring the performance of the system. The second percentage indicates the number of hypothesized words that were erroneous as a percentage of the actual number of words in the test set. This includes both words that were wrongly hypothesized (or deleted) and words that were spuriously inserted. Since the recognizer can, in principle, hypothesize many more spurious words than there are words in the test set, the percentage of errors can actually be greater than 100.
In the example above, using rm1, of the 5681 words in the reference test transcripts 5247 words (92.36%) were correctly hypothesized. In the process the recognizer hypothesized 434 spurious words (these include insertions, deletions and substitutions). You will find your recognition hypotheses in files called *.match in the directory $taskname/result/.
In the same directory, you will also generate files named $taskname/result/*.align in which your hypotheses are aligned against the reference sentences. You can study this file to examine the errors that were made. The list of confusions at the end of this file allows you to subjectively determine why particular errors were made by the recognizer. For example, if the word "FOR" has been hypothesized as the word "FOUR" almost all the time, perhaps you need to correct the pronunciation for the word FOR in your decoding dictionary and include a pronunciation that maps the word FOR to the units used in the mapping of the word FOUR. Once you make these corrections, you must re-decode.
If you are using the built-in method, the line reporting accuracy will look like the following if you used an4.
The meaning of numbers is parallel to the description above, but in this case, the numbers refer to sentences, not to words.
If it is important in your task to be able to distinguish between THIS and THESE, at least one of these two merges should not be performed.
We now have only 27 HMM states, instead of the 33 we began with. In larger data sets with many more triphones, the reduction in the total number of triphones can be very dramatic. The state tying can reduce the total number of HMM states by one or two orders of magnitude.
In the examples above, state-tying has been performed based purely on acoustic-phonetic criteria. However, in a typical HMM-based recognition system such as SPHINX, state tying is performed not based on acoustic-phonetic rules, but on other data driven and statistical criteria. These methods are known to result in much better recognition performance.
where Word1.Word2... is the recognized sequence of words and Wd1.Wd2... is any sequence of words. The argument on the right hand side of Equation 1 has two components: the probability of the feature vectors, given a sequence of words P(feature vectors| Wd1 Wd2 ...), and the probability of the sequence of words itself, P(Wd1 Wd2 ...) . The first component is provided by the HMMs. The second component, also called the language component, is provided by a language model.
The language model provided for this tutorial is a bigram language model.
Here alpha is the language weight. Optimal values of alpha typically lie between 6 and 11.
This page was created by Evandro Gouvêa, adapted from a page created by Rita Singh. For comments, suggestions, or questions, contact the author.


...but I have already installed 2.01 and then 2.02. What to do?
Make sure you've got the original, unedited game. We have downloads here. Forshire's editions often cause this issue.
Make sure you have installed 2.01 BEFORE you installed 2.02.
Make sure you are either starting RotWK through the T3A:Online launcher OR have it hooked (from the Hook menu in the launcher) when you start your RotWK shortcut.
Make sure you are running everything related to the game and T3A:Online as administrator. That includes the launcher, the game shortcut, and the game's .exe files in the installation folder.
If the above solutions don't work for you, please reinstall the game and follow these instructions closely.
Edited by Miraak5, 07 April 2018 - 10:35 AM.


If you havent discovered this little baby, then you might just have learned why your opponents seem always to be able to see you in FRB but you can't see them!
TrackIR is a device you plug into your PC, that reads a tracker on your head or hat and allows you to 'look' around the cockpit as naturally as you would in real life.
WT supports 6DoF or six degrees of freedom, meaning you can look anywhere and the in game camera will follow your movements.


text:
Bottled breast milk program expands to Maine Med, and other hospitals may follow
The health benefits of breastfeeding are widely recognized, so the state's largest hospital is making bottled breast milk available to mothers who can't immediately breastfeed their infants.
By Joe LawlorPortland Press Herald
Melanie Lord feeds her newborn daughter, Lila, pasteurized breastmilk at Maine Medical Center on March 20. The hospital now gives pasteurized breastmilk from donors to some babies in their first four days of life. The milk comes from the Mothers' Milk Bank Northeast, a Massachusetts-based nonprofit that accepts donated milk and distributes it to hospitals. Press Herald photo by Brianna Soukup
In the first few days after Melanie Lord gave birth to her daughter, Lila, at Maine Medical Center this month, she couldn't produce enough breast milk for her newborn – a common problem that now has a solution at the Portland hospital.
Lord, 42, of Brunswick, obtained donated bottled breast milk from a relatively new program at Maine Med. The human breast milk comes from the Mothers' Milk Bank Northeast, a Massachusetts-based nonprofit that accepts donated milk and distributes it to 90 hospitals, including Maine Med. Several other Maine hospitals are considering joining the program.
"I didn't have enough (breast milk) at that time," Lord said. "By getting the donated breast milk, I was able to keep my daughter out of the NICU (neonatal intensive care unit)."
About 25 percent of all mothers who give birth at Maine Med qualify for the program, most commonly because of a combination of factors, such as delayed lactation, low birthweight, infant's low blood sugar and, occasionally, because of certain medications the mother is taking, said Sharon Economides, program manager for lactation and childbirth education at Maine Med.
"Mom is doing everything she can, but sometimes it takes a few days for her milk supply to come in. This gives us a bridge to that point," Economides said.
Cynthia Cohen, director of client relations for Mothers' Milk Bank Northeast, said the nonprofit is in discussions with five other Maine hospitals – which she would not identify – to add the service. Most hospitals in Massachusetts now offer donated breast milk, and the Mothers' Milk Bank program has more than doubled in size over the past five years. Northern Light Eastern Maine Medical Center in Bangor has a program at its neonatal intensive care unit.
As the health benefits of breastfeeding have become clearer over the decades, more women have chosen to do so, with breastfeeding rates going from about 20 percent in the early 1970s to 82 percent in 2015, the latest year statistics were available, according to the U.S. Centers for Disease Control. Eighty-five percent of Maine mothers breastfed their babies at least once, the Maine CDC said.
Benefits of breastfeeding include lower risks of asthma, ear and gastrointestinal infections, obesity, diabetes and avoiding other common medical issues.
Maine's policies, practices encourage breast feeding
About 3,000 babies are born at Maine Med every year, while 12,500 babies are born in Maine annually. The program started in the neonatal intensive care unit at Maine Med, but has since expanded to the general maternity wing for mothers who meet the medical risk factor criteria. Mothers who don't meet the criteria but still feel that they need bottled breast milk can purchase it for $13.50 for a 100-milliliter bottle
Women also can donate breast milk to the program by going to Maine Med. The breast milk is sent from Maine Med to the milk bank in Newton Upper Falls, Massachusetts.
Kara Kaikini, breastfeeding consultant for the Maine CDC, said the idea has "really taken off," and she expects that many Maine hospitals will be starting programs similar to Maine Med's in the next several years.
The bottled breast milk program is designed to tide mothers over to when lactation is more productive. Lord used the bottles for the three days she was in the hospital and for several days after she went home. She said she had three risk factors that qualified her for the program – low birthweight, gestational diabetes and her daughter, Lila's, low blood sugar. Lila was a full-term baby, but her birthweight was low at 4 pounds, 15 ounces.
Nationally, there has been steady growth in bottled breast milk programs, which went from producing fewer than 1 million ounces of breast milk per year in the early 2000s to dispensing more than 6.5 million in 2018, according to the Human Milk Banking Association of North America.
Naomi Bar-Yam, executive director of Mothers' Milk Bank Northeast, expects that with the benefits so obvious, the bottled breastmilk programs should continue to expand.
"The research has been so compelling. It's kind of a no-brainer at this point," Bar-Yam said. "Parents are getting the message. Doctors are getting the message that we need to take this seriously."
Joe Lawlor can be contacted at 791-6376 or at:
Twitter: joelawlorph
maine medical center

redpajama_set_name: RedPajamaCommonCrawl


When the news reached Leghorn that Felix was deprived of his wealth and rank, the merchant commanded his daughter to think no more of her lover, but to prepare to return to her native country.
A few days after, the Turk entered his daughter's apartment and told her hastily that he had reason to believe that his residence at Leghorn had been divulged and that he should speedily be delivered up to the French government; he had consequently hired a vessel to convey him to Constantinople, for which city he should sail in a few hours.
Taking with her some jewels that belonged to her and a sum of money, she quitted Italy with an attendant, a native of Leghorn, but who understood the common language of Turkey, and departed for Germany.
From Leghorn to Naples (calling at Civita Vecchia to land any who may prefer to go to Rome from that point), the distance will be made in about thirty-six hours; the route will lay along the coast of Italy, close by Caprera, Elba, and Corsica.
This oath was no longer a vain menace; for the fastest sailer in the Mediterranean would have been unable to overtake the little tartan, that with every stitch of canvas set was flying before the wind to Leghorn.
A thrill of delicious excitement ran through Rebecca's frame, from her new shoes up, up to the leghorn cap and down the black braid.
Then I hustled the nondescript chickens to market, replacing them with the White Leghorns.
On a personal note, my only confirmed loss from a bird of prey was a White Leghorn.
Senator Richard Gordon is the Foghorn Leghorn of our public life.
The city is literally devastated," said Leghorn Mayor Filippo Nogarin, adding that "a crazy amount of rain" pummeled the area in just a few hours, AP reported.


All of us have a family. We are all born into one, some of us have created one, or we have done both. Family is a very important part of life. God designed families to grow together and share love. Knowing how to develop family relationships will help you throughout your journey and will improve every area of your life. I say this because it is very hard to go to work or school and be a high performer when things at home are on the rocks. It's hard to enjoy vacations and recreation time when there is no peace in the house. When the home is in order, it will positively affect all areas of your life. In this article, I am going to share with you four ways to improve your home life.
We have to learn how to get enjoyment out of life. We are so busy these days and we always have things going on. Making time to do recreational activities with your family can break the rush of day-to-day life. We have to have balance. It is true that work and school take up a large part of our lives, but it should never be our life. If you don't know how to have fun, or even worse, you don't make time to have fun, you are making a huge mistake. Smiles should be your goal. You should look for ways to make your spouse, your kids, your parents, your siblings and all other family members smile. Creating these fun moments could be times that are remembered forever.
If you have a large family and all of your fun times are as a group, you are missing out on a really special opportunity. The one-on-one date is a huge moment of blessing. I remember my mother came to visit me once for a three day weekend. We spent the entire time together. I don't own a coffee machine so every morning I would go get her coffee from a nearby gas station and have it ready for her when she woke up. We cooked breakfast together, went exercising at a nearby track, went to some really nice restaurants and just spent time visiting. When it was time for her to leave, I was very sad because I didn't want her to go but she had to work the next day. As she was packing, her flight was cancelled, and she got to stay another day. It was a really great memory for both of us and I will remember it forever.
I am a huge advocate for spending one-on-one time with another family member. It makes it that much more special. When we go as a group, we have to split our attention in multiple different ways and tend to everyone. However, during one-on-one time, all your energy can be given to one person and it makes that time much more powerful. There are a lot of opportunities out there to get some one-on-one time. For example, go to a Father/Daughter dance, hire a baby sitter so you and your spouse spend a night out, or even take a drive with a sibling to go get some ice cream. The only limitation that you have is your own imagination.
Another great memory of mine was with my father. He and I were driving home from Tucson, AZ one afternoon. I grew up in a town that was about an hour from the city. The trip to Tucson is basically a straight shot so when he turned down a different road I as curious as to why. We pulled up to this wooded fence area and I saw horse stables. Some folks met up to talk with us and my dad talked with them a bit. This place did horseback rides for the public and my Dad and I joined the next riding group. It was the first time I ever rode a horse alone and it was a lot of fun. The ride was about an hour long and my Dad and I rode side by side on a dirt trail. I felt like a cowboy from the old days. As a kid, it meant the world to me to be like a cowboy. This momentous time was completely unplanned and I didn't expect it at all. It was completely random and it was one of the highlights from my childhood. My father was well known for doing things like that and it made life fun and exciting. The random act is a great way to produce moments that you will treasure and it will definitely bring joy to your family life.
Gary Chapman is the author of the book The Five Love Languages and once I finished it, it changed my view towards relationships completely. I've mentioned the love languages in a previous article and I believe that knowing these will give you huge insights on how to show love to another person. I get a lot of enjoyment when I try to figure out someone's love language, but instead of guessing or analyzing, you can just ask someone. Either way, once you figure it out, you will have the means to stimulate that person's emotions to an incredible degree.
Take some time to determine every one of your family member's love language and then use that knowledge to benefit everyone. For example, my sister's love language is gifts. When she wants to show love to someone, she will give or buy them something. On the flip side, she feels most loved when she is given a gift. Knowing this, if I really wanted to bring my sister joy, I could give her a gift card and take her on an all-day shopping spree. This alone would have her elated for a very long time and it would create a moment that she will treasure forever. Determine another person's love language and then do things that will show love in that way and you will never go wrong.


Classically styled pendant featuring one 6x4mm oval Emerald gemstone surrounded by 1.5mm brilliant cut Cubic Zirconia stones. Overall dimensions: 15 long, including fixed bail, 8mm wide. Complete with 9ct gold 18 inch Prince of Wales chain.
Presented in a luxury gift box - great gift!


text:
Q: Cannot read property 'length' of null, javascript. Check if given string contain letters: p & t Im trying debug my JS mini projects. A few of them, shows:

Uncaught TypeError: Cannot read property 'length' of null
at task(xx)

Those mini-projects works, but I would like to delete all the bugs.

This task is about: check whether a given string contains equal number of p's and t's.
I followed message of the bug, as I understand if the length of variable is null or 0, that means this is undefined and cannot be validated. So, unsuccesfully, I tried use this piece of code:
if (p < 0 || t< 0)
or
if (task55word == null)

The basic code is here:
task55 = () => {
  const task55word = document.getElementById("task55").value;
  const task55ans = document.getElementById("task55ans")
  const wordP = task55word.toLowerCase().match(/p/g);
  const wordT = task55word.toLowerCase().match(/t/g);
  const p = wordP.length;
  const t = wordT.length;
  if (p == t) {
 task55ans.textContent = 'equal';
  } else {
    task55ans.textContent = 'not equal';
  }
};

What I have done wrong?

A: Try this code. match returns null if nothing matches.
task55 = () => {
  const task55word = document.getElementById("task55").value;
  const task55ans = document.getElementById("task55ans");
  const wordP = task55word.toLowerCase().match(/p/g) || "";
  const wordT = task55word.toLowerCase().match(/t/g) || "";
  const p = wordP.length;
  const t = wordT.length;
  if (p == t) {
    task55ans.textContent = 'equal';
  } else {
    task55ans.textContent = 'not equal';
  }
};

redpajama_set_name: RedPajamaStackExchange


text:
Ben Shapiro calls Squid Game 'communist and anti-capitalism propaganda'
Ben Shapiro has reviewed Squid Game, calling the record-breaking Netflix series "communist."
"It is a dystopian thriller and so, the politics are very communistic in nature," Shapiro said in his YouTube review of the Korean drama.
He describes the basic premise of Squid Game as a cross between The Hunger Games and Parasite.
Shapiro says that the series' politics are "extraordinarily far to the left" and questions why it has such a big budget and international platform for this reason.
"The basic idea is that capitalism provides so little opportunity that you would rather play a game where you might get shot just to make money, than attempt to live out your life in the regular world," Shapiro says.
"Everybody sort of goes back to the regular life and then, of course, they end up going back to the game because they have no options in the outside world. And this really brings us to the central, sort of parasite aspect, of Squid Game. It is a dystopian thriller and so the politics are very communistic in nature. The politics for this very large budget show, which is being shown on Netflix, which is a high market cap tech company, are extraordinarily far to the left. The basic idea is that capitalism provides so little opportunity that you would rather play a game where you might get shot just to make money, then attempt to live out your life in the regular world."
The show's creator Hwang Dong-hyuk has explained the meaning of Squid Game, telling Variety: "I wanted to write a story that was an allegory or fable about modern capitalist society, something that depicts an extreme competition, somewhat like the extreme competition of life."
"These days we are, in fact, living in a deeply unfair and economically challenging world," Hwang told The Hollywood Reporter.
Squid Game is about hundreds of cash-strapped players who accept a strange invitation to compete in children's games. Inside, a tempting prize awaits — with deadly high stakes unknown to contestants when the games begin.
Hwang also explained that he wanted viewers to think about who created the systems we as a society live in and who benefits from it.
"Communicating the message that you should not be dragged along by the competitive flow of society, but that you should start thinking about who has created the whole system—and whether there is some potential for you to turn back and face it," he said.
One response to "Ben Shapiro calls Squid Game 'communist and anti-capitalism propaganda'"
MAD.IS.ON
Lmao exactly


text:
Le dème de Tenée, en  / Dímos Tenéas, est un ancien dème du  district régional de Corinthie, en Grèce. Depuis 2010, il est fusionné au sein du dème des Corinthiens.

Selon le recensement de 2011, la population du dème compte .

Le dème tire son nom de la cité antique de Ténée qui, selon Pausanias, aurait été fondée par des prisonniers troyens à la suite de la guerre de Troie.

Notes et références

Voir aussi

Liens externes

Dème dans le Péloponnèse
Ancien dème en Grèce
Corinthie

redpajama_set_name: RedPajamaWikipedia


text:
Extreme poverty is a threat to national security – says Momoudu Kargbo
On Air Now 10:00 am - 11:45 am
Faith Based Movie
HomeAYV NewsExtreme poverty is a threat to national security – says Momoudu Kargbo
The three-day forum panel carried out an in-depth review of the following goals 'ending poverty in all its forms everywhere, end hunger, achieve food security, improved nutrition, promoting sustainable agriculture and ensuring healthy lives among Sierra Leoneans.
The summit was aimed at achieving gender equality and empower women and girls, build resilient infrastructure, promote inclusive and sustainable industrialization, foster innovation, conserve and sustainably use the oceans, seas and marine resources for sustainable development, and the same time strengthen the implementation and revitalization the global partnership for Sustainable Development.
Momoud Kagbo told the panel which was convened under the auspices of the United Nations Economic and Social Council (ECOSOC), that Sierra Leone had reduced extreme poverty by thirty one percent in 2004 and about 14 percent in 2015.
He assured the panel that in the past years, the government of Sierra Leone had prioritized extreme poverty intervention in key areas within governance, and his government, government through its national commission for social action provided employment and conditional cash transfer to more than 10,000 youths, including unconditional cash transfer to more than 21,004 vulnerable households in nine of the fourteen districts in Sierra Leone.
"We remain committed to supporting our post war affected persons with special focus on war widows, war wounded and amputees, child orphans and victims of sexual violence and about 9,600 women have been provided with basic social protection packages in the past year" Momoudu Kargbo stated.
"We have also continued to provide massive assistance to thousands of survivors of the Ebola disease outbreak. Indeed we find it exceedingly unacceptable that we currently having to incur an annual food import bill of 350 million when domestic potential are enormous towards minimizing this expenditure and household poverty.
Furthermore, as minister of finance and economic development of Sierra Leone, he said his government through the enactment of a comprehensive public finance management bill was working assiduously towards boosting the country's domestic revenue, and it scaled up service delivery capacity of the state.
He furthered that on May 29, 2017, the Ministry of Finance and Economic Development together with the World Bank jointly organized a Sierra Leone Development Finance forum which was chaired by President Ernest Koroma.
The aim objective of the forum was to unlock private investment in Sierra Leone, put an end to extreme poverty and promote shared prosperity and development.
Parminda Barr, the World Bank Country Manager to Sierra Leone said $2.5 billion would be available to support the private sector in fragile countries like Sierra Leone, but critics of the government have denounced the administration for relying on donor partners to maintain economy unsustainable and the World Bank's new approach of financing development and reducing poverty.
The minister spoke about government efforts towards curbing corruption during an interview and he went further to state that his government is fighting hard to end corruption in Sierra Leone, and the Anti-corruption Commission was set up to oversee, prosecute and investigate all issues of corruption.
The recent initiative on eradicating all forms of corruption is the Pay no Bribe campaign and it an arrangement jointly funded by the department for international development, in which corruption hotlines were set up to report corruption cases.


feather drift in feather air.
pecan shell shards on the stair.
Less song for the power line to bear.
Coo-OO-oo she sang, my dear.
Copyright © 2018 Cecily Parks. Used with permission of the author. This poem originally appeared in Tin House, Spring 2018.


Extraction, Filling & Packaging for Dispensaries, Caregivers & Growers in the Northeast U.S.
Don't see what you're looking for? Contact us today. We are committed to the highest level of customer support and service.


text:
OLIVIA MUNN TO STAR IN ALEPPO FOR MILU ENTERTAINMENT
It has been announced that award-winning actress Olivia Munn (X-Men Apocalypse, The Newsroom) is attached to play the lead in the drama feature ALEPPO for MiLu Entertainment. The announcement follows her recent casting as the lead in the much-anticipated action title REPLAY at the 2020 Virtual Cannes Market. Munn will also lead the thrilling social drama THE GATEWAY, currently in post-production.
ALEPPO is the story of a Syrian boy refugee and a UN journalist (Munn), brought together by tragedy in their escape from Syria to survive.Currently in pre-production, ALEPPO will be directed by award-winning Brazilian director David Schurmann from an original script by Beto Dantas. Academy Award-winning Art Director/Set Decorator Brigitte Broch and Golden Globe-nominated composer Antonio Pinto are also attached, with Juan Bergaz of Bergaz Productions serving as executive producer.
"Olivia is a strong, dynamic leading actress, whose passion for the project under David's direction will bring this powerful and important story to life," said Producer Andre L III, CEO of MiLu Entertainment.
Schurmann directed the Brazilian entry LITTLE SECRET (PEQUENO SEGREDO) to the 89th Academy Awards in the Best Foreign-Language category, Broch won her Academy Award decorating Baz Luhrmann's MOULIN ROUGE set, Pinto got his Golden Globe nomination composing the Brazilian lyric original song "Despedida".
Additionally, MiLu Entertainment is producing EFM/Berlin'20 Hot Title THE THICKET which stars Peter Dinklage, Noomi Rapace, Sophia Lillis, Charlie Plummer and is currently holding ALEPPO sales rights.
September 25, 2020 September 23, 2020 ALEPPO, Art, Community, Culture, Director David Schurmann, Freshly Pressed, Independent Film, Indie Film, MiLu Entertainment, Olivia Munn, OLIVIA MUNN TO STAR IN ALEPPO FOR MILU ENTERTAINMENT, Reading, Refugee, Syria
Previous Previous post: Celebrating The Birthdays Of Two Wonderfully Inspiring People
Next Next post: Autumn Has Arrived!!


text:
Q: Why does value / 100 return 0? I have the following code:
int aGrade=7;
int asteriskCount=((aGrade/100)*50);
System.out.print(asteriskCount);

for(int i=0; i<=asteriskCount; i++)
{
    System.out.print("*");
}

asteriskCount evaluates to zero, so the code only prints one asterisk instead of the four I expected. Why?

A: It's due to integer division. By using a floating value for 100.0 instead of 100, the division is performed using floating point.
Try
int asteriskCount = (int)((aGrade/100.0)*50);
                                  ^^^^^

A: There are a few problems with your code:
Integer division:
Since you're using integers, here's what actually happens in the calculation:
(aGrade/100) -> 0
((aGrade/100) * 50) = (0 * 50) -> 0

How to fix:
You can either use floating point calculation (as suggested in other answers), or simply invert the order of operations:
int asteriskCount = ((aGrade * 50) / 100);
That will solve the problem, and will result in 3 instead of 0.
Lack of rounding:
As mentioned above, even if you correct your calculation, you'll still get the asterisk count of 3 and not 4. The reason is that you're not rounding the result.
How to fix:
You can achieve rounding in integer division by adding half of the amount your dividing by before the division takes place. Why does this work ? Since it simulates the way you round things normally, i.e. adding 0.5 after the division and trimming the remainder.
In this example the calculation should look like this:
int roundUp = (100 / 2);
int asteriskCount = ((aGrade * 50 + roundUp) / 100);

And there you have it, now you get 4 !
Incorrect iteration length:
There's yet another problem that will cause you to get the wrong number of asterisks printed, and that is that your iterations prints one asterisk too many. This is the reason that at the moment, you're seeing one asterisk instead of none (remember that the original calculation results in 0).
When you want to iterate a certain number of times, remember that if you start from 0, you should finish before the number is reached, otherwise, you're iterating once too many.
How to fix:
The iteration should be:
for (int i=0; i<asteriskCount; i++) {
    System.out.print("*");
}

Good luck !

A: You may try this:
Double asteriskCount= Double((aGrade/100.0)*50);

The problem is integer/integer division is int. You need to cast it to Double and store it in a double.
But if you want to store it in an int then cast it in int.
int asteriskCount = (int)((aGrade/100.0)*50);

A: Yes it is due to integer division, like he said.
When you divide 5 by 2, the answer is 2.5. However if you use a integer to store the answer, it will appear as just 2.
Likewise, when you divide 7 by 100, your answer is 0.07, therefore the stored integer value is just 0.
Try this:
double aGrade = 7;
int asteriskCount = (int)((aGrade/100)*50);

A: When you do a division like this aGrade/100 = 7/100 Java stores only the integer part of the result. i.e 7/100 = 0. So it doesn't matter what you multiply this result with afterwords, the answer will always be zero. This is called an integer division. If you want this calculation to return 0.07 you have to do divide with a double. i.e. : (aGrade/100.0)*50 and then when you multiply with 50, to use the result in your for loop, cast the second calculation to an int:
int asteriskCount = (int)((aGrade/100.0)*50);


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The Allied successes in reducing the Marne Salient and of the advance before Amiens led to a reevaluation of the possibility of ending the war in 1918. Much strategic thinking to this point had been guided by the fact that the second wave of 2 million American troops would not be in place until April 1919.
Generalissimo Ferdinand Foch was an advocate for a broad advance to push the German army back across the Rhine. A critical part of his plan would be executed by a brand new American Army, under Pershing's command, which was just organized in August. After extensive negotiations, it was agreed that in a preliminary operation, this U.S. First Army would be given the job of reducing the St. Mihiel Salient which had threatened Verdun and the Allies' east-west rail communications for four years. Then, two weeks later they would shift their forces west for another offensive on the old Verdun battlefields. In this position they anchored the right end of a broad group of Allied offensives extending from the Channel, through the Somme sector and over through the Champagne to the immediate left flank of the Americans.
The coordinated assault would--in Foch's thinking--prevent Ludendorff from shifting reenforcements from hot spot to hot spot. Also, each of the individual operations had the potential for a dramatic breakthrough similar to what had happened in August at the opening of the Amiens attack. Such a breakthrough was not to be repeated in any of the sectors, but the strategy was sufficient to pressure Germany to seek terms in less than two months.
Fr. IV Army, Launched 26 Sept.
This is a first-class and long overdue study of the opening battles of World War I on the Western Front and the armies that fought them. It is also the first book in English on the Battle of the Frontiers fought in the Ardennes. Perhaps the most valuable part of this analysis is contained in the first two chapters in which Dr. Zuber examines the very different doctrine and training methods used by Germany and France. Not surprisingly, he finds German doctrine, tactical training and staff leadership vastly superior to the often slapdash French system. Specifically, the Battle of the Frontiers proved the superiority of German battle tactics and frontline leadership, as well as superior understanding of the importance of reconnaissance despite the frequent breakdowns of command and control at the divisional and corps level. The Germans emphasized patrols at all tactical levels and in all arms. While the French focused on maneuver, the Germans sought battlefield superiority through movement and firepower, including the use of their powerful, high angle-of-fire howitzer. Unlike the rigid French Army system of detailed written orders, Germany stressed flexibility and initiative among company and platoon commanders and even NCOs. These habits of command were reinforced by frequent peacetime field exercises at all levels of command. By comparison, French prewar training regimes were uneven and irregular.
Moreover, the Battle of the Frontiers did not prove the efficacy of either the German or French battle plans. Since prewar plans were not executed by either side, victory was the result of small unit successes based on superior German tactics and training. Also, the author contends, French defeat in the first battles of 1914 could not be laid at the feet of Grandmaison and his theories of offense á outrance, which were adopted too late to influence French 1914 tactics.
This book is thankfully full of those clear, useful maps often missing from military histories. The author made great use of German primary sources, including war diaries, as well as more limited and often fragmentary French sources. By 1914, as the author asserts, the German Army had reached its pinnacle, having learned to fight outnumbered and win even in the difficult terrain of the Ardennes. Therefore, it is fitting that this monograph concentrates on its operations.
The Battle of the Frontiers, Ardennes 1914, Terence Zuber, Tempus, 2007, 314 pages, maps, photos, schematic sketches, orders of battle, end notes, glossary, ISBN 978 0 7524 4424 6, $34.96 cloth. Dr. Zuber is a retired U.S. Army Major and the author of Inventing the Schlieffen Plan: German War Planning 1871-1914 (Oxford 2002) and German War Planning 1891-1914: Sources and Interpretations (Boydell & Brewer, 2004).
This past August the Turner Classic Movies channel aired some rarely seen pre-code Barbara Stanwyck movies. Of special interest was Ever in My Heart (1933) directed by Archie Mayo and written by Bertram Millhauser and Beulah Marie Dix. In just 70 minutes, Ever in My Heart tells the story of Mary Archer's (Barbara Stanwyck) marriage to Hugo Wilbrandt (Otto Kruger) in pre-World War One New England and the subsequent events that destroy their happiness.
I had never seen or even heard of Ever in My Heart prior to this airing and I was completely surprised by its pace, its pre-code frankness and its explicit condemnation of a xenophobic America.
Mary Archer is expected to marry her cousin Jeff (Ralph Bellamy) when he returns from studying in Europe. Instead she falls in love with Hugo, a university friend who has accompanied him back to Archerville in New England. Hugo woos her by playing "Du, Du Liegst Mir im Herzen" on the family piano. Her family opposes her marriage to a foreigner, but the couple proceeds anyway. Hugo gets a position as a college chemistry professor and the young couple is very happy with Teddy, their young son (Ronnie Cosby) and the family dog, a dachshund.
Hugo becomes an American citizen, and during the ensuing celebration, a variety of songs are sung by the party guests indicating their national origins: "(I Wish I Was in) Dixie," "La Marseillaise," "Rule Britannia," "Yankee Doodle," "A Stein Song" and "Auld Lang Syne". There's no mistaking that Hugo is loved by his fellow faculty members and adored by his wife and young son. But the morning newspaper headline proclaims that Germany has invaded Belgium, and there's nothing but misery ahead for Mary and Hugo.
The sinking of the Lusitania places further strains on the family. Dismissed from his job, Hugo is snubbed by the community. Teddy dies (in a very moving scene while Hugo sings to him), some neighborhood children brutally stone the dachshund and Hugo is forced to shoot him. When they are nearly destitute during the winter, Mary's grandmother offers Hugo a job in the family mill if he will change his name. Hugo refuses but sends Mary off to her family, saying he will join her later. Just before Christmas Mary receives a letter from Hugo. He writes, "They let me be a citizen, but they won't let me be an American." Hugo abandons Mary and returns to Germany.
After America enters the war, Mary, Jeff and her younger brother Sam (Frank Albertson) all do their bit. Mary goes to Europe and while working in a canteen sees Hugo dressed as an American soldier. He's actually the spy that Jeff is looking for. Mary hides Hugo in her room and after making love for the last time she poisons their wine and they die together. The Rainbow Division is saved, and Mary and Hugo are gone.
Ever in My Heart received decidedly mixed reviews 75 years ago. For example, The New York Times felt that it "dipped into the limbo of forgotten war scenarios." And that "the tragedy is meaningless to this new generation." But it praised "the warm and intelligent acting of Barbara Stanwyck and Otto Kruger." This was Stanwyck's 16th film and her first truly tragic role. Kruger would play another German spy in Hitchcock's Saboteur (1942).
I feel that any movie prescient enough to portray a xenophobic American society prior to the 1942 internment of Japanese-Americans and the harassment of Muslim-Americans in today's post-9/11 society deserves to be re-examined. Unfortunately, Ever In My Heart is not available on DVD, so Turner Classic Movies channel is its only venue at the moment. It is not in frequent rotation, so if you're interested in seeing it you'll need to follow the TCM channel listings. It's worth the search.
The following are thanked for their contributions to this issue of the Trip-Wire: Christina Holstein, Tom Jones, Tony Langley Andrew Melomet, Ray Morrisette, Tony Noyes, Bradley Omanson, John Shetler, Len Shurtleff, and Kimball Worcester. Until next month, your editor, Mike Hanlon.


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Goal 8 - Decent work and economic growth · Kenya · Livelihoods and Income · Sep 13, 2021
Dairy farming is empowering women in Kenya
Prior to participating in ChildFund's Agriculture, Dairy and Economic Development (ADED) project in Kenya, mother of six Josephine did not see rearing dairy cows as a business opportunity.
Like many Maasai mothers in Emali, all she wanted was a cow to produce milk for her children.
But now, following training and support through ChildFund, not only does Josephine have a reliable milk supply for her children, she has a reliable income source from selling milk, she is part of the Samli Dairy Cooperative that gives her protection as a vendor, and perhaps most importantly, she now has peace of mind that there is enough money to send her children to school.
"This project has totally transformed our lives," says Josephine.
For Emali women, milk is a tool for economic independence. Women own milk in the Maasai culture while men own the livestock.
Josephine's home in Emali is surrounded by pasture for her cows, and it's a rare sight in rural Kenya where overgrazing is common place.
Through the ChildFund training, Josephine has learned about pasture management, including the importance of not over grazing and also the importance of fencing.
"We were taught that a cow's health is your wealth and that really stuck with me," Josephine says.
"It was like a dream come true. I saw all my financial problems vanishing right before my eyes!"
The Samli Dairy Cooperative is giving farmers like Josephine a consistent and reliable market for milk and she is one of more than 800 farmers benefiting from the collective.
Prior to the formation of the Samli Dairy Cooperative, farmers like Josephine had to depend on unreliable milk vendors who would pay low milk prices and sometime fail to collect their milk. Being in the cooperative also gave her peace of mind during her recent pregnancy.
"I couldn't imagine how I could have handled the brokers when I was expectant, they take advantage of women, old and pregnant, who cannot run after them for their milk money," she says.
"The cooperative came at the opportune time allowing me to rest and wait for my payment through mobile money at the end of each month."
Josephine is also registered as a supplier for the Oltinka Milk Collection Centre, one of two fully operating milk collection centres in Emali. Additionally there are three fully operating milk collection points.
Currently she is milking one cow with an average of 5 kilograms of milk collected per day. About 3kg is sold and about 2kg is consumed in her own household. A second cow is expected soon to start producing milk for her business too.
Previously when milk was sold on cash basis only, she didn't have any savings. The little money she received would go towards buying the household basics items in small quantities and it was not economical for her family.
Josephine has been able to join three Voluntary Savings and Loan Associations (VSLAs) groups which distribute resources in rural Kenya, and she is now saving money. She also is eligible to take a loan that she can service through her milk business.
The future looks bright for Josephine and her family. Now she has a reliable income stream, improving milk production is her next focus.
ChildFund's Agriculture, Dairy and Economic Development in Emali
In Emali, communities are vulnerable to drought which can destroy their crops and leave families without food and income.
The ChildFund dairy farming project that has been evolving in Kenya for nearly four years with funds from the New Zealand's Ministry of Foreign Affairs and Trade (MFAT) and Kiwi supporters. The project has created around 5000 employment opportunities in the area.
Farming experts from New Zealand have worked with ChildFund's local partner, the Emali Dedicated Children's Agency, local officials and farmers from the Maasai and Kamba tribes around Emali since 2015.
Agricultural initiatives they have helped implement include introducing a new more drought resistant fodder and new cattle varieties; extensive training in animal care and basic vet skills; stock husbandry; farm and business management training; and a youth vet club programme.
There are now three fully operating milk collection points and two fully operating milk collection centres. The Masimba plant has a 5,000 litre cooling tank, milk testing equipment and an office for managing the intake of raw milk from farmers and ensuring these farmers get paid on time for their milk.
As the farming area in Emali is vast, ChildFund established five facilities so that farmers can deliver the raw milk quickly and efficiently. If a farmer delivers to a nearby milk collection point, it will then be quickly transferred to one of the milk collection centres to be stored in the industrial-sized cooling tank.
Culturally, owning cattle is very significant for Maasai farmers. Men own the cows and women own the milk, as the women do the milking and feed it to their children. By developing a milk business where farmers can use their existing livestock and milk supply to earn a regular income, families can now provide for their children (even during drought).
Further reading about the ChildFund Agriculture, Dairy and Economic Development project.
Kiwi farming ingenuity opens doors in Kenya through ChildFund and MFAT partnership
Update of our Agriculture, Dairy and Economic Development project in Emali, Kenya
ChildFund launches a new milk processing plant in rural Kenya, in partnership with MFAT
Your donations to this project are match funded, meaning every $1 you give, the project will receive $3! If you would like to support our ongoing work in Emali, Kenya and the ADED project, please donate now.


Jungle Safari on Elephant spotting various wildlife, mammals & bird-spices..
Jeep Safari, Bird Watching, Canoeing on traditional dug-out canoe..
Visit to the Tharu village to witness local culture & tradition..
Follow tiger tracks through the jungle with your trained naturalist guide.
Journey deep into the jungle atop an elephant Or by Jeep.
Visit Nepal's largest elephant breeding center.
Once home to a royal hunting reserve, Chitwan is now widely recognised as one of the best national parks in Asia, and this trip includes walking, canoeing and driving safaris through its lush landscapes..
Take a boat along the Rapti river, which shelters about a quarter of the world's remaining gharial population..
Jump on a jeep safari to Bishazari Tal and enjoy a guided bird-watching experience..
Explore the sights in old Kathmandu and Bhaktapur..
Search for tigers in Chitwan National Park..
Trek the foothills of the Annapurna Range..
Go on an elephant safari in Chitwan National Park.
See a variety of endangered animal species, including one horned rhinos.
Walk through a dense, tropical jungle.
One-horned Rhinoceros & Royal Bengal Tigers.
Canoeing & visit to Elephant Breeding Center.
Visit the Korea National Arboretum, explore its gardens and forests.
Travel to the Pocheon Art Valley, popular for shooting Korean TV programs.
Enjoy the fragrance of Mediterranean herbs in Herb Island.
Village Tour and Visiting an Elephant Breeding Centre.
Bird Watching and enjoyment of the Chitwan way of life.
Sightseeing Tour kathmandu and pokhara.
Visit a UNESCO Natural Heritage Site in Nepal.
Discover the beauty and diversity of the Terai belt region.
Go on a jeep safari and search for Bengal tigers.


Many of us look back on our school days and rate history as one of the more boring subjects we had to study. If you happened to be one of those unfortunates, could I ask you to indulge for a short time in a history lesson about the Passover? I think you'll be blessed since the institution of the Lord's Supper takes place within the context of a Passover meal.
According to the apostle Paul, the Sunday service in the first century church in Corinth was chaotic. Before we see why, let's be reminded about the origin of this particular church of God. This community came into existence through the ministry of the Apostle Paul. "One night the Lord spoke to Paul in a vision: 'Do not be afraid; keep on speaking, do not be silent. For I am with you, and no one is going to attack and harm you, because I have many people in this city.' So Paul stayed for a year and a half, teaching them the word of God." Through his ministry "many of the Corinthians who heard him believed and were baptised." (See Acts 18:8-11) Though they were God's people, this was a church awash with problems; their carnal behaviour displayed itself when the saints assembled on the first day of the week to partake of the Lord's Supper. What should have been an occasion for holy communion, in every sense of that word, was anything but holy. One of the problems had to do with their previous practice of idolatry.
The behaviour of the believers in Corinth when they gathered on Sunday to worship the Lord was deplorable. Their "love feast" was anything but loving and their holy communion was anything but holy – so much so that Paul said, "your meetings do more harm than good." (1 Corinthians 11:17) There was division among the believers, the "love feast" (a shared meal) was not shared with everyone and, as a result, those who were poor were neglected. To make things worse, there were some who even got drunk! And these were the very people who had come to remember the most loving act the world had ever witnessed – the death of the Lord Jesus Christ for sinners.
How are we to understand these words of Jesus? Controversy has surrounded our Lord's words: some hold that the words of Jesus are to be understood literally, while others hold that his words are to be understood figuratively.
The crowd asks Jesus, "What miraculous sign will you give that we may see it and believe you? What will you do? Our fathers ate the manna in the desert; as it is written: 'He gave them bread from heaven to eat.'" (John 6:30-31) (Apparently the previous day's miracle of feeding 5,000 had not convinced them!) What they are saying is this: God met the needs of our forefathers; what are you going to do for us? Jesus picks up on their words and says, "It is my Father who gives you the true bread from heaven. For the bread of God is he is come down from heaven and gives life to the world." (vs 32-33) Jesus, unknown to them, is moving the conversation in a particular direction that will allow him teach about his atoning death. Jesus personalises his comments about the bread of life. "For the bread of heaven is he who gives life to the world." This bread imparts life. "'Sir,' they said, 'from now on give us this bread. '" (vs 34) Did they really want what Jesus had to offer? No. They were misunderstanding Jesus and thinking he was offering to meet their physical needs, permanently. That's why they said, "from now on give us this bread."
"How can this man give us his flesh to eat?" (John 6:52) Thinking Jesus was speaking literally, the crowd's question is natural. (Remember that they were already confused by Jesus' words that he came down from heaven when in fact they knew he came from Nazareth.) The theme of Jesus' teaching throughout this dialogue is the giving of himself as the perfect sacrifice resulting in eternal life for all who believe.


Sub:- Disbursement of Salary to Central Government employees working in the State of Kerala on 9th September 2016 on account Of "ONAM" Festival.
ONAM is the State Festival of Kerala. The Festival commences on 5th September 2016 and culminates cn 14-09-2016. This is also a social festival and is celebrated by one and all. It had been the normal practice to draw and disburse the salary and pension of the employees and pensioners in the month in which the festival falls well prior to the commencement of the festival.
We shall be grateful if orders are issued to all Departments to disburse the salary and pension for the month of September, 2016 by September 2016 in respect of all employees and pensioners in the State of Kerala. A copy of the order issued by your office on 10-08-2015 in this mater is enclosed for ready reference.


Returning to Vienna from Brooklyn NYC the Nat Osborn Band played a small gig at Reigen on the 14th of May as part of their 2014 European Tour.
We got the chance to get up close and personal with the Jazz/Rock Fusion Troupe to bring you some exclusive Snapshots from the show as well as an Interview we filmed afterwards.
Terrific to see these guys again, we are determined to bring them back and put on a show with a crowd that they deserve.