@inproceedings{578c3685020340dea50682ca515b8cbd,
title = "Speech-Based Driver Emotion Recognition",
abstract = "The premise that vehicles bring convenience to human life is to ensure the safety of people in vehicles. However, driver{\textquoteright}s negative emotions are an important cause of risky driving, road rage, and traffic crashes, which seriously endangers traffic safety. In this paper, we proposed a driver emotion recognition method based on driver{\textquoteright}s speech using audio features. Firstly, we extracted 6 features for speech gender recognition. After gender recognition, a combination of gender and MFCCs features were used for negative emotion recognition. Finally, a driver emotion recognition application was developed for function display.",
keywords = "Driver emotion, MFCCs, Psychological state, Speech emotion recognition, Traffic safety",
author = "Haiqiu Tan and Haodong Zhang and Jian Shi and Dongxian Sun and Jie Zhang and Xiaobei Jiang and Wuhong Wang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 12th International Conference on Green Intelligent Transportation Systems and Safety, 2021 ; Conference date: 17-11-2021 Through 19-11-2021",
year = "2023",
doi = "10.1007/978-981-19-5615-7_41",
language = "English",
isbn = "9789811956140",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "577--587",
editor = "Wuhong Wang and Jianping Wu and Ruimin Li and Xiaobei Jiang and Haodong Zhang",
booktitle = "Green Transportation and Low Carbon Mobility Safety - Proceedings of the 12th International Conference on Green Intelligent Transportation Systems and Safety",
address = "Germany",
}