Recognizing Long-Form Speech Using Streaming End-to-End Models
Table 5: Results comparing models trained on search and multidomain data. For the multidomain model, (deletions / insertions / substitutions) are also shown.
['[BOLD] Test set', '[BOLD] WER (%) (D\xa0/\xa0I\xa0/\xa0S)  [BOLD] Search', '[BOLD] WER (%) (D\xa0/\xa0I\xa0/\xa0S)  [BOLD] Multidomain']
In Tab. we compare WERs using models trained on search subset, and the multidomain training set using the Count-Weighted strategy. As can be seen in the table, increasing training data diversity significantly improves performance in a range of conditions. Even on the search test set, we see a 6% relative WER reduction. Performance on the TTS-Audiobook improves dramatically from 49.0% to 4.7%. Similar gains are seen in telephony and the YouTube sets. Interestingly, on the harder call-center test set, multidomain training helps improve performance by 40% relative. From the deletion, insertion and substitution rates shown in the table, it can be seen that the multidomain model has much fewer deletions than the model trained with the search subset. The multidomain model still has high deletions for the call-center test set, likely because of the differences in acoustics compared to the training data – the utterances in this set are typically long and have much longer pauses.