What does a network layer hear? Analyzing hidden representations of End-to-end ASR through speech synthesis
Table 1: Speech recognition word error rates on LibriSpeech with 100 hours of training data.
['[BOLD] Model', '[BOLD] Augmentation', '[BOLD] WER (%) dev-clean', '[BOLD] WER (%) test-clean']
E2E ASR Models We explore two ASR architectures in the experiments. Each convolutional layer is followed by a ReLU activation layer and max-pooing is applied every 2 convolutional layers. The other one is a pure-LSTM model, which has only 5 bidirectional LSTM layers, with downsampling performed after 2, 3 and 4-th layer. The input to the models is 80-dimensional mel-spectrograms. Deltas and accelerations are added and stacked along the channel dimension. Both models are trained with Connectionist Temporal Classification(CTC) loss. We take the models trained on clean set without augmentation data as our baseline ASR models, and the ones trained on noisy set are our noise-robust ASR models.