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Streaming processing of speech audio is required for many contemporarypractical speech recognition tasks. Even with the large corpora of manuallytranscribed speech data available today, it is impossible for such corpora tocover adequately the long tail of linguistic content that's important for taskssuch as open-ended dictation and voice search. We seek to address both thestreaming and the tail recognition challenges by using a language model (LM)trained on unpaired text data to enhance the end-to-end (E2E) model. We extendshallow fusion and cold fusion approaches to streaming Recurrent Neural NetworkTransducer (RNNT), and also propose two new competitive fusion approaches thatfurther enhance the RNNT architecture. Our results on multiple languages withvarying training set sizes show that these fusion methods improve streamingRNNT performance through introducing extra linguistic features. Cold fusionworks consistently better on streaming RNNT with up to a 8.5% WER improvement.

Language model fusion for streaming end to end speech recognition