Emotion Recognition in Human Voice Speech Based on Machine Learning
- DOI
- 10.2991/978-2-494069-45-9_19How to use a DOI?
- Keywords
- Emotion recognition; Machine learning; Emotion feature extraction; Classification algorithms
- Abstract
Emotion recognition of speech is the basis of human-computer interaction interface, and has also made remarkable development in the past decade. This paper summarizes the preprocessing and feature extraction methods and different existing emotion models and database. The speech is collected and converted into signals, and then the extracted features are identified through the speech recognition model. It also explains the still existing shortcomings at the present stage, such as emotional complexity and high-quality emotional corpus is difficult to obtain. In addition, this paper also introduces the future development and optimization direction of speech emotion recognition. It’s important for researchers to accurately describe the association between emotion and acoustic characteristics, and construct a recognition model that is reasonable and as close to the emotion processing mechanism of human brain.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xiaorui Wang PY - 2022 DA - 2022/12/16 TI - Emotion Recognition in Human Voice Speech Based on Machine Learning BT - Proceedings of the 2022 2nd International Conference on Modern Educational Technology and Social Sciences (ICMETSS 2022) PB - Atlantis Press SP - 149 EP - 157 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-45-9_19 DO - 10.2991/978-2-494069-45-9_19 ID - Wang2022 ER -