Latent Sequence Decompositions
Table 2: Wall Street Journal test eval92 Word Error Rate (WER) results across Connectionist Temporal Classification (CTC) and Sequence-to-sequence (seq2seq) models. The Latent Sequence Decomposition (LSD) models use a n=4 word piece vocabulary (LSD4). The Convolutional Neural Network (CNN) model is with deep residual connections, batch normalization and convolutions. The best end-to-end model is seq2seq + LSD + CNN at 9.6% WER.
['[BOLD] Model', '[BOLD] WER']
The previously best reported basic seq2seq model on WSJ WER achieved 18.0% WER Our baseline, also a seq2seq model, achieved 14.8% WER. Main differences between our models is that we did not use convolutional locational-based priors and we used weight noise during training.