Amazon Technologies, Inc.

Jeffrey John Farris

Sravan Babu Bodapati

Katrin Kirchhoff

Monica Lakshmi Sunkara

Srikanth Ronanki

Mohit Gupta

Vivek Govindan

Nilaksh Das

Silviu Mihai Burz

Yide Zou

Devang Kulshreshtha

Jinglun Cai

Nicholas G Aldridge

Bias may be removed from automatic speech recognition model predictions using internal language model estimates. Audio data may be received for speech recognition. The audio data may be processed both through an automatic speech recognition model to produce original word token predictions and masked in different portions of the audio data to produce other word token predictions for the masked audio. A comparison of the original word token predictions and the other word token predictions may provide an estimate of an internal language model for the automatic speech recognition model. This estimate can be used to modify the original word token predictions to remove the lexical bias and produce a speech prediction.

Removing bias from automatic speech recognition models using internal language model estimates

Techniques for personalized batch and streaming speech-to-text transcription of audio reduce the error rate of automatic speech recognition (ASR) systems in transcribing rare and out-of-vocabulary words. The techniques achieve personalization of connectionist temporal classification (CT) models by using adaptive boosting to perform biasing at the level of sub-words. In addition to boosting, the techniques encompass a phone alignment network to bias sub-word predictions towards rare long-tail words and out-of-vocabulary words. A technical benefit of the techniques is that the accuracy of speech-to-text transcription of rare and out-of-vocabulary words in a custom vocabulary by automatic speech recognition (ASR) system can be improved without having to train the ASR system on the custom vocabulary. Instead, the techniques allow the same ASR system trained on a base vocabulary to realize the accuracy improvements for different custom vocabularies spanning different domains.

Personalized batch and streaming speech-to-text transcription of audio

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Crystal Lake, IL, United States of America

Jeffrey John Farris