Addressing ASR Challenges for Stuttered Speech Through a Speech Translation Framework

Abstract

Stuttered speech presents significant challenges for automatic speech recognition (ASR) due to its irregular patterns and the scarcity of annotated data. This limitation hinders the development of robust systems capable of accurately recognizing and processing stuttered speech. To address these issues, this study propose a novel approach that leverages text-to-speech (TTS) technology for data augmentation, enabling the synthesis of realistic stuttered speech to supplement existing datasets. Using this augmented data, this study develop an ASR system within a speech translation framework designed to transform stuttered speech into fluent text.