Google Inc.

Deepti Bhatia

Tara N Sainath

Bo Li

Wei Ping Li

Yu Zhang

Ruoming Pang

Rohit Prakash Prabhavalkar

Yanzhang He

Wei Li

David Rybach

Ke Hu

Liangliang Cao

Ian Mcgraw

Zelin Wu

David Qiu

Ding Zhao

Qiujia Li

A method includes receiving a speech recognition result, and using a confidence estimation module (CEM), for each sub-word unit in a sequence of hypothesized sub-word units for the speech recognition result: obtaining a respective confidence embedding that represents a set of confidence features; generating, using a first attention mechanism, a confidence feature vector; generating, using a second attention mechanism, an acoustic context vector; and generating, as output from an output layer of the CEM, a respective confidence output score for each corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the CEM. For each of the one or more words formed by the sequence of hypothesized sub-word units, the method also includes determining a respective word-level confidence score for the word. The method also includes determining an utterance-level confidence score by aggregating the word-level confidence scores.

Learning word-level confidence for subword end-to-end automatic speech recognition

A method includes receiving audio data encoding an utterance, processing, using a speech recognition model, the audio data to generate speech recognition scores for speech elements, and determining context scores for the speech elements based on context data indicating a context for the utterance. The method also includes executing, using the speech recognition scores and the context scores, a beam search decoding process to determine one or more candidate transcriptions for the utterance. The method also includes selecting a transcription for the utterance from the one or more candidate transcriptions.

Using context information with end-to-end models for speech recognition

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Deepti Bhatia