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arXiv · 2106.10259

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

Katrin Tomanek·Françoise Beaufays·Julie Cattiau·Angad Chandorkar·Khe Chai Sim
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Abstract

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.

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BibTeXRIS

Katrin Tomanek, Françoise Beaufays, Julie Cattiau, Angad Chandorkar, Khe Chai Sim. 2021-06-18. On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech. https://arxiv.org/abs/2106.10259

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