Very Deep Convolutional Networks for End-to-End Speech Recognition
Table 4: Wall Street Journal test eval92 Word Error Rate (WER) results across Connectionist Temporal Classification (CTC) and Sequence-to-sequence (seq2seq) models. The models were decoded without a dictionary or language model.
['[BOLD] Model', '[BOLD] WER']
To our knowledge, the previous best reported WER on WSJ without an LM was the seq2seq model with Task Loss Estimation achieving 18.0% WER in [bahdanau-iclr-2016]. Our baseline, also a seq2seq model, achieved 14.76% WER. Our model is different from that of [bahdanau-iclr-2016] in that we did not use location-based priors on the attention model and we used weight noise.