Answers.Body: <p>Yes you can, a few years ago I made a simple <a href="https://www.computer.org/csdl/proceedings-article/snpd/2017/08022720/12OmNxWuihg" rel="nofollow noreferrer">CNN for a single Arabic phoneme classification</a>. You can use <a href="https://arxiv.org/abs/1712.00866" rel="nofollow noreferrer">spectogram</a> or using <a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/CNN_ASLPTrans2-14.pdf" rel="nofollow noreferrer">MFCC / MFSC</a> as features, as long all data has the same size (use padding or cropping if needed).</p>
  
  <p>You may need RNN if you want to combine some phonemes to recognize a single word or longer.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-17T03:09:12.287Z
Answers.Text: Wow, thanks a lot! Any suggestions for the model shape (how many layers and how many filters)?
Answers.UserDisplayName: Hozaifa Bhutta
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-17T03:16:22.363Z
Answers.Text: Actually, it all depends on your case, for a simple case I think you only need one or two convolutional layers and a single fully-connected layer. I usually use around 30 filters. But, just try it to find your best values :)
Answers.UserDisplayName: malioboro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-17T03:52:00.233Z
Answers.Text: Okay, well I actually I have a full model written down but it isn't running well (not increasing in accuracy). Where should I post my code and give a better description of my model?
Answers.UserDisplayName: Hozaifa Bhutta
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-17T07:39:11.143Z
Answers.Text: You can ask another question if you think there are some errors in your models. But, you [may ask an implementation question that out of topic in AI.SE](https://ai.stackexchange.com/help/on-topic) if your question is about errors in your code/implementation, you should ask that in [Data Science SE](https://datascience.stackexchange.com/)
Answers.UserDisplayName: malioboro
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Answers.CreationDate: 2019-11-19T03:32:59.720Z
Answers.Text: Okay thanks, I put the question on this link, so you can look over it if you want to, 'https://datascience.stackexchange.com/questions/63376/cnn-model-not-converging'
Answers.UserDisplayName: Hozaifa Bhutta
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Answers.CreationDate: 2019-11-22T13:45:51.750Z
Answers.Text: @HozaifaBhutta if this or any answer in the future solved your question please consider [accepting it](https://meta.stackexchange.com/q/5234/179419) by clicking the check-mark. This indicates to the wider community that you've found a solution. There is no obligation to do this.
Answers.UserDisplayName: malioboro
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Answers.CreationDate: 2019-11-28T17:43:11.003Z
Answers.Text: Quick question, I have a 2 layer network (one convolution, 30 filters, and one output layer). What should the dropout amount be?
Answers.UserDisplayName: Hozaifa Bhutta
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-28T21:34:13.090Z
Answers.Text: What do you mean by "amount"?
Answers.UserDisplayName: malioboro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-28T22:39:12.120Z
Answers.Text: So I currently made the dropout rate 35% for my convolution layer. Is this a good rate? Should I add dropout for my output layer?
Answers.UserDisplayName: Hozaifa Bhutta
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-29T00:27:14.457Z
Answers.Text: the dropout rate is one of the hyperparameters that you need to tune, but, in many papers people use 50% rate.
Answers.UserDisplayName: malioboro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-29T00:28:25.013Z
Answers.Text: for placement you can read explanations [here](https://stats.stackexchange.com/questions/240305/where-should-i-place-dropout-layers-in-a-neural-network) and [here](https://stackoverflow.com/questions/38125657/what-layers-should-experience-dropout-when-training-a-neural-network)
Answers.UserDisplayName: malioboro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-12-05T13:50:13.157Z
Answers.Text: How big was your input size? As in how long was each utterance
Answers.UserDisplayName: Hozaifa Bhutta
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-17T01:52:23.817Z
Answers.LastActivityDate: 2019-11-17T01:52:23.817Z
Answers.UserDisplayName: malioboro
Body: <p>I was making a simple phoneme classification model for a 10 week-long class project and I ran into a small question.</p>
  <p>Is it possible to create a model that takes a 1-second (the longest phoneme is 0.2 second but the large image is kept for context) spectrogram as input? Some people suggest creating an RNN for phoneme classification, but can you build a pure CNN phoneme classification model?</p>
  
Tags: <machine-learning><convolutional-neural-networks><ai-design><training>
Title: Can you build a pure CNN phoneme classification model?
UserDisplayName: Hozaifa Bhutta
ThreadText: # Can you build a pure CNN phoneme classification model?
  
  (Asked by: username_0 on 2019-11-17)
  
  I was making a simple phoneme classification model for a 10 week-long class project and I ran into a small question.
  
  Is it possible to create a model that takes a 1-second (the longest phoneme is 0.2 second but the large image is kept for context) spectrogram as input? Some people suggest creating an RNN for phoneme classification, but can you build a pure CNN phoneme classification model?
  
  **Tags:** machine-learning, convolutional-neural-networks, ai-design, training
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-11-17. Score: 1 | Accepted answer
  
  Yes you can, a few years ago I made a simple [CNN for a single Arabic phoneme classification](https://www.computer.org/csdl/proceedings-article/snpd/2017/08022720/12OmNxWuihg). You can use [spectogram](https://arxiv.org/abs/1712.00866) or using [MFCC / MFSC](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/CNN_ASLPTrans2-14.pdf) as features, as long all data has the same size (use padding or cropping if needed).
  
  You may need RNN if you want to combine some phonemes to recognize a single word or longer.
  
  > **Comments:**
  > **username_0**: Wow, thanks a lot! Any suggestions for the model shape (how many layers and how many filters)?  
  > **username_1**: Actually, it all depends on your case, for a simple case I think you only need one or two convolutional layers and a single fully-connected layer. I usually use around 30 filters. But, just try it to find your best values :)  
  > **username_0**: Okay, well I actually I have a full model written down but it isn't running well (not increasing in accuracy). Where should I post my code and give a better description of my model?  
  > **username_1**: You can ask another question if you think there are some errors in your models. But, you [may ask an implementation question that out of topic in AI.SE](https://ai.stackexchange.com/help/on-topic) if your question is about errors in your code/implementation, you should ask that in [Data Science SE](https://datascience.stackexchange.com/)  
  > **username_0**: Okay thanks, I put the question on this link, so you can look over it if you want to, 'https://datascience.stackexchange.com/questions/63376/cnn-model-not-converging'  
  > **username_1**: @HozaifaBhutta if this or any answer in the future solved your question please consider [accepting it](https://meta.stackexchange.com/q/5234/179419) by clicking the check-mark. This indicates to the wider community that you've found a solution. There is no obligation to do this.  
  > **username_0**: Quick question, I have a 2 layer network (one convolution, 30 filters, and one output layer). What should the dropout amount be?  
  > **username_1**: What do you mean by "amount"?  
  > **username_0**: So I currently made the dropout rate 35% for my convolution layer. Is this a good rate? Should I add dropout for my output layer?  
  > **username_1**: the dropout rate is one of the hyperparameters that you need to tune, but, in many papers people use 50% rate.  
  > **username_1**: for placement you can read explanations [here](https://stats.stackexchange.com/questions/240305/where-should-i-place-dropout-layers-in-a-neural-network) and [here](https://stackoverflow.com/questions/38125657/what-layers-should-experience-dropout-when-training-a-neural-network)  
  > **username_0**: How big was your input size? As in how long was each utterance  
  
  ---
  
  