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

• URL: http://arxiv.org/abs/2106.10259v1
• Date: Fri, 18 Jun 2021 17:48:08 GMT
• Title: On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech
• Authors: Katrin Tomanek, Fran\c{c}oise Beaufays, Julie Cattiau, Angad Chandorkar, Khe Chai Sim
• Abstract summary: 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.
• Score: 9.698986579582236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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