Fast, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces
Table 4: Comparison of our chenone-CTC with previous best results on LibriSpeech. “4g” means the official 4-gram LM was used; “NNLM” means a neural LM was used.
['Arch.', 'System', 'LM', 'test-clean', 'test-other']
To achieve best WERs, we use vggTrf stride 4 trained with chenone-CTC, and changed time masking of SpecAugment LD policy to (T=30,mT=10). The transformer LM achieved perplexity 52.35 on the dev set (a combination of dev-clean and dev-other). We then perform n-best rescoring on up to 100-best hypotheses generated by first pass decoding. The oracle error rate of the n-best hypotheses are 1.0% and 2.2% on test-clean and test-other respectively. Our final WERs (2.08%/4.30%) are the best results among all hybrid systems on this widely used benchmark. In this work, we pushed the performance boundary of hybrid ASR using transformer-based acoustic models with context-dependent CTC training.