Recognizing Long-Form Speech Using Streaming End-to-End Models
Table 7: Results using models trained on multidomain data, with various LSTM state initialization. Baseline uses the typical zero-state initialization, RSS and RSP stand for random state sampling and random state passing, respectively.
['[BOLD] Test set', '[BOLD] WER (%)  [BOLD] Baseline', '[BOLD] WER (%)  [BOLD] RSS', '[BOLD] WER (%)  [BOLD] RSP']
Tab. As can be seen in the table, RSS and RSP perform comparably with the baseline on the in-domain test sets. However, on the unseen domain of call-center utterances, these techniques significantly improve performance over the baseline: RSS improves WER by 8% relative, and RSP improves it by 27% relative. Compared to the baseline model, RSP reduces the deletion rate from 26.5% to 17.1%. When combined with multidomain training, RSS and RSP can improve performance further and are thus complementary.