Listen, Attend and Spell
Table 1: WER comparison on the clean and noisy Google voice search task. The CLDNN-HMM system is the state-of-the-art system, the Listen, Attend and Spell (LAS) models are decoded with a beam size of 32. Language Model (LM) rescoring was applied to our beams, and a sampling trick was applied to bridge the gap between training and inference.
['[BOLD] Model', '[BOLD] Clean WER', '[BOLD] Noisy WER']
During training the model is conditioned on the correct previous characters but during testing mistakes made by the model corrupt future predictions. This improved our results on the clean and noisy test sets to 14.1% and 16.5% WER respectively when no language model rescoring was used. With language model rescoring, we achevied 10.3% and 12.0% WER on the clean and noisy test sets, respectively.