Abstract:
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.
