4.6 Article

Emotional Speech Recognition Method Based on Word Transcription

期刊

SENSORS
卷 22, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/s22051937

关键词

emotion recognition; speech recognition; crowd emotion recognition; affective computing; distance learning; e-learning; artificial intelligence

资金

  1. Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan [BR11765535]

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This article presents an emotional speech recognition method for recognizing student emotions during online exams in distance learning due to COVID-19. The method achieves an accuracy of 79.7% for the Kazakh language and can be widely applied to recognize emotions in different languages. It analyzes human speech using emotionally charged words stored in a code book to determine the presence of emotions.
The emotional speech recognition method presented in this article was applied to recognize the emotions of students during online exams in distance learning due to COVID-19. The purpose of this method is to recognize emotions in spoken speech through the knowledge base of emotionally charged words, which are stored as a code book. The method analyzes human speech for the presence of emotions. To assess the quality of the method, an experiment was conducted for 420 audio recordings. The accuracy of the proposed method is 79.7% for the Kazakh language. The method can be used for different languages and consists of the following tasks: capturing a signal, detecting speech in it, recognizing speech words in a simplified transcription, determining word boundaries, comparing a simplified transcription with a code book, and constructing a hypothesis about the degree of speech emotionality. In case of the presence of emotions, there occurs complete recognition of words and definitions of emotions in speech. The advantage of this method is the possibility of its widespread use since it is not demanding on computational resources. The described method can be applied when there is a need to recognize positive and negative emotions in a crowd, in public transport, schools, universities, etc. The experiment carried out has shown the effectiveness of this method. The results obtained will make it possible in the future to develop devices that begin to record and recognize a speech signal, for example, in the case of detecting negative emotions in sounding speech and, if necessary, transmitting a message about potential threats or riots.

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