The Microsoft 2017 Conversational Speech Recognition System
Table 2: Acoustic model performance by senone set, model architecture, and for various frame-level combinations, using an N-gram LM. The “puhpum” senone sets use an alternate dictionary with special phones for filled pauses.
['Senone set', 'Architecture', 'devset WER', 'test WER']
The results are based on N-gram language models, and all combinations are equal-weighted. In the past we had used a relatively small vocabulary of 30,500 words drawn only from in-domain (Switchboard and Fisher corpus) training data. While this yields an out-of-vocabulary (OOV) rate well below 1%, our error rates have reached levels where even small absolute reductions in OOVs could potentially have a significant impact on overall accuracy. We supplemented the in-domain vocabulary with the most frequent words in the out-of-domain sources also used for language model training: the LDC Broadcast News corpus and the UW Conversational Web corpus. Boosting the vocabulary size to 165k reduced the OOV rate (excluding word fragments) on the eval2002 devset from 0.29% to 0.06%.