Emotion Detection via Voice and Speech Recognition

International Journal of Cyber Behavior, Psychology and Learning Pub Date : 2023-11-10 DOI:10.4018/ijcbpl.333473

Rohit Rastogi, Tushar Anand, Shubham Kumar Sharma, Sarthak Panwar

引用次数: 0

Abstract

Emotion detection from voice signals is needed for human-computer interaction (HCI), which is a difficult challenge. In the literature on speech emotion recognition, various well known speech analysis and classification methods have been used to extract emotions from signals. Deep learning strategies have recently been proposed as a workable alternative to conventional methods and discussed. Several recent studies have employed these methods to identify speech-based emotions. The review examines the databases used, the emotions collected, and the contributions to speech emotion recognition. The Speech Emotion Recognition Project was created by the research team. It recognizes human speech emotions. The research team developed the project using Python 3.6. RAVDEESS dataset was also used since it contained eight distinct emotions expressed by all speakers. The RAVDESS dataset, Python programming languages, and Pycharm as an IDE were all used by the author team.

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通过语音和语音识别进行情绪检测

从语音信号中检测情感是人机交互(HCI)的一个难点。在语音情感识别的文献中，已经使用了各种已知的语音分析和分类方法来从信号中提取情感。深度学习策略最近被提出作为传统方法的可行替代方案并进行了讨论。最近的几项研究使用了这些方法来识别基于语言的情绪。这篇综述检查了使用的数据库，收集的情绪，以及对语音情绪识别的贡献。该研究小组创建了语音情感识别项目。它能识别人类的语言情感。研究小组使用Python 3.6开发了这个项目。我们还使用了RAVDEESS数据集，因为它包含了所有说话者表达的八种不同的情绪。作者团队使用了RAVDESS数据集、Python编程语言和Pycharm作为IDE。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Cyber Behavior, Psychology and Learning Social Sciences-Education

CiteScore

1.10

自引率

0.00%

发文量

期刊介绍： The mission of the International Journal of Cyber Behavior, Psychology and Learning (IJCBPL) is to identify learners’ online behavior based on the theories in human psychology, define online education phenomena as explained by the social and cognitive learning theories and principles, and interpret the complexity of cyber learning. IJCBPL offers a multi-disciplinary approach that incorporates the findings from brain research, biology, psychology, human cognition, developmental theory, sociology, motivation theory, and social behavior. This journal welcomes both quantitative and qualitative studies using experimental design, as well as ethnographic methods to understand the dynamics of cyber learning. Impacting multiple areas of research and practices, including secondary and higher education, professional training, Web-based design and development, media learning, adolescent education, school and community, and social communication, IJCBPL targets school teachers, counselors, researchers, and online designers.