On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech

While current state-of-the-art Automatic Speech Recognition (ASR) systems achieve high accuracy on typical speech, they suffer from significant performance degradation on disordered speech and other atypical speech patterns. Personalization of ASR models, a commonly applied solution to this problem,...

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Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Tomanek, Katrin, Beaufays, Françoise, Cattiau, Julie, Chandorkar, Angad, Khe Chai Sim
Format: Article
Language:English
Subjects:
Automatic speech recognition
Copying
Customization
Electronic devices
Performance degradation
Speech
Voice communication
Voice control
Voice recognition
Online Access:Get full text
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Summary:While current state-of-the-art Automatic Speech Recognition (ASR) systems achieve high accuracy on typical speech, they suffer from significant performance degradation on disordered speech and other atypical speech patterns. Personalization of ASR models, a commonly applied solution to this problem, is usually performed in a server-based training environment posing problems around data privacy, delayed model-update times, and communication cost for copying data and models between mobile device and server infrastructure. In this paper, we present an approach to on-device based ASR personalization with very small amounts of speaker-specific data. We test our approach on a diverse set of 100 speakers with disordered speech and find median relative word error rate improvement of 71% with only 50 short utterances required per speaker. When tested on a voice-controlled home automation platform, on-device personalized models show a median task success rate of 81%, compared to only 40% of the unadapted models.
ISSN:2331-8422