@inproceedings{79bb917963374a1780f68bb33ba0ee57,
title = "Federated Intelligent Terminals Facilitate Stuttering Monitoring",
abstract = "Stuttering is a complicated language disorder. The most common form of stuttering is developmental stuttering, which begins in childhood. Early monitoring and intervention are essential for the treatment of children with stuttering. Automatic speech recognition technology has shown its great potential for non-fluent disorder identification, whereas the previous work has not considered the privacy of users' data. To this end, we propose federated intelligent terminals for automatic monitoring of stuttering speech in different contexts. Experimental results demonstrate that the proposed federated intelligent terminals model can analyze symptoms of stammering speech by taking personal privacy protection into account. Furthermore, the study has explored that the Shapley value approach in the federated learning setting has comparable performance to data-centralised learning.",
keywords = "Computer Audition, Federated Learning, Healthcare, Stuttering Monitoring",
author = "Yongzi Yu and Wanyong Qiu and Chen Quan and Kun Qian and Zhihua Wang and Yu Ma and Bin Hu and Schuller, \{Bjorn W.\} and Yoshiharu Yamamoto",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/ICASSP49357.2023.10097263",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
address = "United States",
}