Frame-Level Stutter Detection

Abstract

Previous studies on the detection of stuttered speech have focused on classification at the utterance level (e.g., for speech therapy applications), and on the correct insertion of stutter events in sequence into an orthographic transcript. In this paper, we propose the task of frame-level stutter detection which seeks to identify the time alignment of stutter events in a speech utterance, and we evaluate our approach on the stutter correction task. Limited previous work on stutter correction has relied on simple signal processing techniques and only been evaluated on small datasets. Our approach is the first large scale data-driven technique proposed to identify stuttering probabilistically at the frame level, and we make use of the largest available stuttering dataset to date during training. Predicted frame-level probabilities of different stuttering events can be used in downstream applications for Automatic Speech Recognition (ASR) as either additional features or part of a speech preprocessing pipeline to clean speech before analysis by an ASR system.