Self-supervised learning using unlabeled speech with multiple types of speech disorder for disordered speech recognition
Ryoichi Takashima, Takeru Otani, Ryo Aihara, Tetsuya Takiguchi, Shinya Taguchi · 2024 · Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2024)
This paper tackles a critical barrier in accessible speech technology: automatic speech recognition (ASR) systems perform poorly for people with speech disorders because they are trained almost exclusively on typical speech. The authors from Kobe University and Mitsubishi…
speech recognition · speech disorders · machine learning · self-supervised learning · assistive technology