Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge
Abstract
The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin. The challenge comprises three tracks: (1) SED, which aims to develop systems for detection of stuttering events; (2) ASR, which focuses on creating robust systems for recognizing stuttered speech; and (3) Research track for innovative approaches utilizing the provided dataset. We utilizes an open-source Mandarin stuttering dataset AS-70, which has been split into new training and test sets for the challenge. This paper presents the dataset, details the challenge tracks, and analyzes the performance of the top systems, highlighting improvements in detection accuracy and reductions in recognition error rates. Our findings underscore the potential of specialized models and augmentation strategies in developing stuttered speech technologies.
Keywords
Cite
@article{arxiv.2409.05430,
title = {Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge},
author = {Hongfei Xue and Rong Gong and Mingchen Shao and Xin Xu and Lezhi Wang and Lei Xie and Hui Bu and Jiaming Zhou and Yong Qin and Jun Du and Ming Li and Binbin Zhang and Bin Jia},
journal= {arXiv preprint arXiv:2409.05430},
year = {2024}
}
Comments
8 pages, 2 figures, accepted by SLT 2024