Automatic Detection of Anger and Aggression in Speech Signals
DOI:
https://doi.org/10.52575/2712-746X-2023-50-4-944-954Keywords:
speech data, speech databases, classification, classification methods, low-level descriptors, anger recognition, aggression recognitionAbstract
The article discusses the issue of detecting anger and aggression in a speech signal. The fundamental differences between anger and aggression are considered. A review of solutions for recognizing destructive behavior in the form of anger and aggression using a speech signal, presented in various modern publications, was carried out. The main classification methods used to solve the problem of recognizing emotions from speech are considered. Information support in the form of Russian-language and non-Russian-language speech databases used to train models for recognizing emotions is analyzed. The main problems of using speech databases are formulated. The issue of choosing speech signal parameters used to classify emotions in general and destructive behavior in particular is considered. Implemented anger recognition on the Russian-language Dusha database using two approaches and three classification methods.
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