In societies with well developed internet infrastructure, social media is the leading medium of communication for various social issues especially for breaking news situations. In rural Uganda however, public community radio is still a dominant means for news dissemination. Community radio gives audience to the general public especially to individuals living in rural areas, and thus plays an important role in giving a voice to those living in the broadcast area. It is an avenue for participatory communication and a tool relevant in both economic and social development.This is supported by the rise to ubiquity of mobile phones providing access to phone-in or text-in talk shows. In this paper, we describe an approach to analysing the readily available community radio data with machine learning-based speech keyword spotting techniques. We identify the keywords of interest related to agriculture and build models to automatically identify these keywords from audio streams. Our contribution through these techniques is a cost-efficient and effective way to monitor food security concerns particularly in rural areas. Through keyword spotting and radio talk show analysis, issues such as crop diseases, pests, drought and famine can be captured and fed into an early warning system for stakeholders and policy makers.
Overview
This paper presents a very interesting application of speech keyword spotting techniques; the aim is to listen to continuous streams of community radio in Uganda in order to spot keywords of interest related to agriculture to monitor food security concerns in rural areas. The lack of internet infrastructure results in farmers in rural areas using community radio to share concerns related to agriculture. Therefore accurate keyword spotting techniques can potentially help researchers flag up areas of interest. The main engineering challenge that the study deals with is the fact that there isn’t a lot of training data available for languages like Lugandu.
Detailed Comments
 Section 3
1. The word “scrapped” should be replaced with “scraped”.
2. Its not entirely clearly what is meant by “as well as an alternative keyword in form of a stem”. What is meant by “stem” in this context? Maybe a citation or an example would make it more clear. 
3. The corpus of keywords supposedly contains 193 distinct keywords however the models in Section 4 are only trained to discriminate between 10 randomly sampled keywords. I don’t understand why this is. Training on the full corpus would allow the network to see more training data and consequently might result in more accurate models. 
Section 4
1. The authors refer to Equation 3 in Section 4.1, but I think the reference is to Equation 2.
2. The 1-D CNN has 14 layers but it is not clear to me what the size of the final network is. It would be useful to provide more details of the architecture and a comment about the network size either in terms of number of parameters, number of computations or size of the network weights on disk. 
3. The figure for  the Siamese network should be numbered. 
4. The authors say that the inputs to the ResNets are of shape 32x100. What do these dimensions refer to? Are their 32 frequency bins? Are their 100 frames as inputs? What are the parameters of the FFT computation? Do 100 frames correspond to a second of audio? 
5. How many parameters are their in the Siamese network? 
6. Its not clear to me how the Siamese networks are used during inference. The authors say they use the L1 distance to determine if a given test example is similar to a given keyword. How are the examples for the keyword of interest selected? Are all training examples used and the scores average? Are the training examples averaged first to form a centroid vector for  the keyword? 
Comments on the methodology
One of the main challenges in this work is the fact that there isn’t a large number of training examples available. However the authors still train relatively large acoustic models with 14 layers for 1-D CNN and a ResNet for the Siamese architecture. There are several studies for keyword spotting on mobile devices that aim to train tiny networks that consume very little power [1] [2]. I think it would be more appropriate to start with architectures similar to these due to the small size of the training dataset. Additionally, it would also be very useful to try and identify languages that are phonetically similar to Lugandu and that have training datasets available for speech recognition. Acoustic models trained on this data can then be adapted to the keyword spotting task using the training set collected for this task. Note that the languages need to have only a small amount of phonetic overlap in order for these acoustic models to be useful starting points for training keyword spotting models. 
Comments on the evalation
The keyword spotting models presented here are trained with the intention of applying them to streaming radio data. However, the models are trained and tested on fixed chunks of audio. A big problem with this experimental design is that the models can overfit to confounding audio cues in the training data. For example, if all training data are in the form of 1 second audio chunks and all chunks have at least some silence at the start, then the models learn that a keyword is always preceded by a certain duration of silence. This is not the case when keywords occur in the middle of sentences in streaming audio data. The fact that the evaluation is also performed on individual chunks of audio fails to evaluate how the trained models would behave when presented streaming audio. I am certain that when applied to streaming audio these models would false trigger very often, however this fact isn’t reflected by measuring precision and recall on fixed chunks of test audio. 
A more principled evaluation strategy would be to present results in the form of detection-error tradeoff (DET) curves [1], [2]. Here the chunks with examples of the keyword in question can be used to measure the number of false rejects by the system. And long streams of audio data that do not contain the keyword should be used to measure the false alarms. Given that its relatively easy to collect streams of radio data, the false alarms can be measured in terms of the number of false alarms per unit time (minutes, hours or days). This evaluation strategy roughly simulates the streaming conditions in which this model is intended to be deployed. Furthermore, DET curves present the tradeoff between false alarms and false rejects as a function of the operating threshold, which provides much more insight into the performance/accuracy of the trained models. 
Summary
I think the area of application of this work is extremely interesting, however the training and evaluation methodologies have to be updated in order to realistically measure the way this system might perform in real-world test conditions. 
References
[1] Small-footprint Keyword Spotting Using Deep Neural Networks
[2] Efficient Voice Trigger Detection for Low Resource Hardware