计算机与数字工程2025，Vol.53Issue(5)：1251-1256,6.DOI:10.3969/j.issn.1672-9722.2025.05.007
基于迁移学习的儿童口吃检测及其严重程度算法研究
Research on Children's Stuttering Detection and Its Severity Algorithm Based on Transfer Learning
蔡雨成 1潘文林1
作者信息
- 1. 云南民族大学电气信息工程学院 昆明 650000
- 折叠
摘要
Abstract
At present,the detection of stuttering in China is mainly through the subjective evaluation of language experts,and there is a lack of objective medical aids.At the same time,there is not enough Chinese children's stuttering dataset to match the large number of parameters of the deep network and form a model with good detection effect.In response to this phenomenon,this paper uses the UClass data set and personal collection of Chinese children's stuttering data,and uses the Yolov5 algorithm to detect the spectrogram based on transfer learning,so as to evaluate the severity of domestic children's stuttering.The experimental results show that the algorithm and its model can effectively detect the type of speech repetition,extension and interjection of Chinese stut-tering,and calculate the language efficiency score(SES)according to its duration to indicate the severity of stuttering,which is con-venient for early detection of children's stuttering disorder,this helps children's physical and mental health.关键词
深度学习/Yolo算法/口吃/目标检测/迁移学习Key words
deep learning/Yolo algorithm/stuttering/object detection/transfer learning分类
信息技术与安全科学引用本文复制引用
蔡雨成,潘文林..基于迁移学习的儿童口吃检测及其严重程度算法研究[J].计算机与数字工程,2025,53(5):1251-1256,6.
