Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Table 2: Frame classification data extracted from TIMIT.
['[EMPTY]', 'Train', 'Development', 'Test']
For the phoneme recognition task, we use TIMIT, which comes with time segmentation of phones. We use the official train/development/test split and extract frames for the frame classification task. Note that due to sub-sampling at the DeepSpeech2 convolutional layers, the number of frames decreases by a factor of two after each convolutional layer. The possible labels are the 60 phone symbols included in TIMIT (excluding the begin/end silence symbol h#). Consequently, the comparison between layers before and after convolutions is not entirely fair. To investigate this effect, we ran the trained convolutions without strides during feature generation for the classifier.