Improved Recognition of the Speech of People with Parkinson's Who Stutter

Jonghwan Na, Xiuwen Zheng, Bowon Lee, Mark Hasegawa-Johnson

Abstract: Stuttering is a speech disorder often associated with neurological conditions, including Parkinson’s disease (PD). Despite advancements in modern automatic speech recognition (ASR) technologies, today’s systems still face challenges in accurately recognizing dysarthric speech, particularly when stuttering is present. In this study, we propose a novel stuttered speech data augmentation approach to improve dysarthric speech recognition. We utilize typical speech data from LibriSpeech to generate artificial stuttered speech by applying Voice Activity Detection and Forced Alignment techniques to accurately identify word boundaries, and integrating an adaptive stuttering filter to simulate severe stuttering patterns. Additionally, dysarthric speech data from individuals with PD, collected by the Speech Accessibility Project (SAP), is integrated into the model. Our experimental results demonstrate that the proposed augmentation approach outperforms existing methods in enhancing the recognition of stuttered speech. Furthermore, fine-tuning the ASR systems with SAP data yields additional performance improvements for both stuttering and non-stuttering individuals with PD.