Recognizing Long-Form Speech Using Streaming End-to-End Models
Table 6: Results using models trained on search, 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']
Finally, we present results using random state sampling and random state passing techniques. For RSS, we only sample initial states for the encoder LSTMs, since we empirically found it to work better than sampling initial states of both encoder and projection network. In Tab. As can be seen, on the search test set, the baseline model, which uses all-0 initial LSTM states, and both RSS and RSP perform similarly, obtaining WERs of 4.9%, 4.8%, and 5.0%, respectively. On the TTS-Audiobook set, RSS improves WER by 37% relative, and RSP improves WER by 67% relative. While RSP consistently improves performance, it alone is insufficient to completely account for acoustic dissimilarities between training and testing: For example, on the YouTube test set, even though RSP improves WERs by 11% relative, the overall WER is still as high as 61.7%. The effect of state-passing is similar to multidomain training; on the TTS-Audiobook set, the deletion rates were reduced from 43.6 percentage points to 6.9.