The Microsoft 2017 Conversational Speech Recognition System
Table 4: Perplexities and word errors with session-based LSTM-LMs (forward direction only). The last line reflects the use of 1-best recognition output for words in preceding utterances.
['Model inputs', 'PPL devset', 'PPL test', 'WER devset', 'WER test']
There is a large perplexity reduction of 21% by conditioning on the previous word context, with smaller incremental reductions from adding speaker change and overlap information. The table also compares the word error rate with the full session-based model to the baseline, within-utterance LSTM-LM. As shown in the last row of the table, some of the perplexity gain over the baseline is negated by the use of 1-best recognition output for the conversation history. However, the perplexity degrades by only 7-8% relative due to the noisy history.