  Comprehending the overall intent of an utterance helps a listener recognize
the individual words spoken. Inspired by this fact, we perform a novel study of
the impact of explicitly incorporating intent representations as additional
information to improve a recurrent neural network-transducer (RNN-T) based
automatic speech recognition (ASR) system. An audio-to-intent (A2I) model
encodes the intent of the utterance in the form of embeddings or posteriors,
and these are used as auxiliary inputs for RNN-T training and inference.
Experimenting with a 50k-hour far-field English speech corpus, this study shows
that when running the system in non-streaming mode, where intent representation
is extracted from the entire utterance and then used to bias streaming RNN-T
search from the start, it provides a 5.56% relative word error rate reduction
(WERR). On the other hand, a streaming system using per-frame intent posteriors
as extra inputs for the RNN-T ASR system yields a 3.33% relative WERR. A
further detailed analysis of the streaming system indicates that our proposed
method brings especially good gain on media-playing related intents (e.g. 9.12%
relative WERR on PlayMusicIntent).
