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2020 INTERSPEECH INTERSPEECH 2020

Staged Knowledge Distillation for End-to-End Dysarthric Speech Recognition and Speech Attribute Transcription

Abstract

This study proposes a staged knowledge distillation method to build End-to-End (E2E) automatic speech recognition (ASR) and automatic speech attribute transcription (ASAT) systems for patients with dysarthria caused by either cerebral palsy (CP) or amyotrophic lateral sclerosis (ALS). Compared with traditional methods, our proposed method can use limited dysarthric speech more effectively. And the dysarthric E2E-ASR and ASAT systems enhanced by the proposed method can achieve 38.28% relative phone error rate (PER%) reduction and 48.33% relative attribute detection error rate (DER%) reduction over their baselines respectively on the TORGO dataset. The experiments show that our system offers potential as a rehabilitation tool and medical diagnostic aid.

🌉 Interdisciplinary Bridge — Machine Learning and Speech & Audio
🧭 Keyword Pioneer — speech attribute transcription
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors

Yuqin Lin , Longbiao Wang , Sheng Li , Jianwu Dang , Chenchen Ding

Topics

Machine Learning > Application Areas > Knowledge Distillation Speech & Audio > Recognition > Automatic Speech Recognition Speech & Audio > Analysis > Clinical Speech Analysis Machine Learning > Learning Types > Multi-Task Learning Deep Learning > Techniques > Knowledge Distillation

Keywords

knowledge distillation automatic speech recognition clinical speech analysis end-to-end model phone error rate dysarthric speech dysarthric speech recognition speech attribute transcription
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