Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Neural networks have become ubiquitous in automatic speech recognition systems. While neural networks are typically used as acoustic models in more complex systems, recent studies have explored end-to-end speech recognition systems based on neural networks, which can be trained to directly predict text from input acoustic features. Although such systems are conceptually elegant and simpler than traditional systems, it is less obvious how to interpret the trained models.
In this work, we analyze the speech representations learned by a deep end-to-end model that is based on convolutional and recurrent layers, and trained with a connectionist temporal classification (CTC) loss. We use a pre-trained model to generate frame-level features which are given to a classifier that is trained on frame classification into phones. We evaluate representations from different layers of the deep model and compare their quality for predicting phone labels. Our experiments shed light on important aspects of the end-to-end model such as layer depth, model complexity, and other design choices.
The authors conduct an analysis of CTC trained acoustic models to determine how information related to phonetic categories is preserved in CTC-based models which directly output graphemes. The work follows a long line of research that has analyzed neural network representations to determine how they model phonemic representations, although to the best of my knowledge this has not been done previously for CTC-based end-to-end architectures.
The results and analysis presented by the authors is interesting, although there are some concerns I have with the conclusions that the authors draw that I would like to clarify these points. Please see my detailed comments below.
- In analyzing the quality of features learned at each layer in the network, from the description of the experiments in the paper it would appear that the authors only feed in features corresponding to a single frame at time t, and predict the corresponding output label. In the paper, the authors conclude that (Line 159--164) "... after the 5th recurrent layer accuracy goes down again. One possible explanation to this may be that higher layers in the model are more sensitive to long distance information that is needed for the speech recognition task, whereas the local information which is needed for classifying phones is better captured in lower layers." This is a plausible explanation, although another plausible explanation that I'd like to suggest for this result is the following:
It is know from previous work, e.g., (Senior et al., 2015) that even when predicting phoneme output targets, CTC-based models with recurrent layers can significantly delay outputs from the model. In other words, the features at a given frame may not contain information required to predict the current ground-truth label, but this may be shifted. Thus, for example, it is possible that if a window of features around the frame at time t was used instead of a single frame, the conclusions vis-a-vis the quality of the recurrent layers in the model would be very different. An alternative approach, would be to follow the procedure in (Senior et al., 2015) and constrain the CTC loss function to only allow a certain amount of delay in predicting labels. I think it is important to conduct the experiments required to establish whether this is the case before drawing the conclusions in the paper. 
Reference: A. Senior, H. Sak, F. de Chaumont Quitry, T. Sainath and K. Rao, "Acoustic modelling with CD-CTC-SMBR LSTM RNNS," 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Scottsdale, AZ, 2015, pp. 604-609. 
- In analyzing phoneme categories I notice that the authors use the full 61 label TIMIT phoneme set (60 after excluding h#). However, it is common practice for TIMIT to also report phoneme recognition results after mapping the 61 label set down to 48 following (Lee and Hon, 89). In particular, this maps certain allophones into the same category, e.g., many closures are mapped to the same class. It would be interesting to confirm that the same trends as identified by the authors continue to hold in the reduced set.  
Reference: Lee, K-F., and H-W. Hon. "Speaker-independent phone recognition using hidden Markov models." IEEE Transactions on Acoustics, Speech, and Signal Processing 37.11 (1989): 1641-1648.
- There have been many previous works on analyzing neural network representations in the context of ASR apart from the references cited by the authors. I would suggest that the authors incorporate some of the references listed below in addition to the ones that they have already listed:
* Nagamine, Tasha, Michael L. Seltzer, and Nima Mesgarani. "On the Role of Nonlinear Transformations in Deep Neural Network Acoustic Models." INTERSPEECH. 2016.
* Nagamine, Tasha, Michael L. Seltzer, and Nima Mesgarani. "Exploring how deep neural networks form phonemic categories." Sixteenth Annual Conference of the International Speech Communication Association. 2015.
* Yu, Dong, et al. "Feature learning in deep neural networks-studies on speech recognition tasks." arXiv preprint arXiv:1301.3605 (2013).
- In the context of grapheme-based CTC for ASR, I think the authors should cite the following early work:
* F. Eyben, M. Wöllmer, B. Schuller and A. Graves, "From speech to letters - using a novel neural network architecture for grapheme based ASR," 2009 IEEE Workshop on Automatic Speech Recognition & Understanding, Merano, 2009, pp. 376-380.
- Line 250: "... and micro-averaging F1 inside each coarse sound class." What is meant by "micro-averaging" in this context?
- Minor comments and typographical errors:
* Line 135: "We train the classifier with Adam [22] with default parameters ...". Please specify what you mean by default parameters.
* In Figure 1 and Figure 2: The authors use "steps" when describing strides in the convolution. I'd recommend changing this to "strides" for consistency with the text.
* In Figure 1. d: The scale on the Y-axis is different from the other figures which makes the two figures in (c.) and (d.) not directly comparable. Could the authors please use the same Y-axis scale for all figures in Figure 1.