Google Inc.

Ian Mcgraw

Tara N Sainath

Yu Zhang

Rohit Prakash Prabhavalkar

Yanzhang He

Qiao Liang

Liangliang Cao

Shaojin Ding

David Qiu

Qiujia Li

Wei Ping Li

Bo Li

Quan Wang

Trevor D Strohman

Arun Narayanan

Wei Li

Ke Hu

Rami Botros

Weiran Wang

Yangzhang He

Tom O'Malley

Rajeev Rikhye

Deepti Bhatia

Ding Zhao

Xin Wang

Dongseong Hwang

Robert David

Rina Panigrahy

A method including receiving a speech recognition result corresponding to a transcription of an utterance spoken by a user. For each sub-word unit in a sequence of hypothesized sub-word units of the speech recognition result, using a confidence estimation module to: obtain a respective confidence embedding associated with the corresponding output step when the corresponding sub-word unit was output from the first speech recognizer; generate a confidence feature vector; generate an acoustic context vector; and generate a respective confidence output score for the corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the confidence estimation module. The method also includes determining, based on the respective confidence output score generated for each sub-word unit in the sequence of hypothesized sub-word units, an utterance-level confidence score for the transcription of the utterance.

Multi-task learning for end-to-end automated speech recognition confidence and deletion estimation

An automated speech recognition (ASR) model includes a first encoder, a first encoder, a second encoder, and a second decoder. The first encoder receives, as input, a sequence of acoustic frames, and generates, at each of a plurality of output steps, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The first decoder receives, as input, the first higher order feature representation generated by the first encoder, and generates a first probability distribution over possible speech recognition hypotheses. The second encoder receives, as input, the first higher order feature representation generated by the first encoder, and generates a second higher order feature representation for a corresponding first higher order feature frame. The second decoder receives, as input, the second higher order feature representation generated by the second encoder, and generates a second probability distribution over possible speech recognition hypotheses.

Unified cascaded encoder ASR model for dynamic model sizes

A computer-implemented method includes receiving a sequence of acoustic frames corresponding to an utterance and generating a reference speaker embedding for the utterance. The method also includes receiving a target speaker embedding for a target speaker and generating feature-wise linear modulation (FiLM) parameters including a scaling vector and a shifting vector based on the target speaker embedding. The method also includes generating an affine transformation output that scales and shifts the reference speaker embedding based on the FiLM parameters. The method also includes generating a classification output indicating whether the utterance was spoken by the target speaker based on the affine transformation output.

Optimizing personal VAD for on-device speech recognition

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

Growing community of inventors

Mountain View, CA, United States of America

Ian Mcgraw