Speech emotional characteristic extraction method for speech emotion recognition

Abstract

The invention relates to a speech emotional characteristic extraction method for speech emotion recognition, which comprises the following steps of: 1) performing FFT calculation on speech signals to obtain uniform passband energy; 2) by taking the passband energy as a characteristic, calculating an F-Ratio value of speech emotion information at each frequency point to obtain a distribution map of the F-Ratio value of speech emotion information on each frequency band; 3) obtaining a nonuniform Mel subband filter according to the distribution map of the F-Ratio value of speech emotion information on each frequency band, wherein the bandwidth of each passband of the nonuniform Mel subband filter is in inverse proportion to the corresponding F-Ratio value; and 4) acquiring energy output of each subband of a speech signal to be recognized by using the nonuniform Mel subband filter so as to obtain the speech emotional characteristic of the speech signal to be recognized. The speech emotional characteristic extraction method can improve the authentication of multiple classes of emotions, and improves the emotion recognition performance compared with the traditional MFCC characteristic.

Description

technical field [0001] The invention relates to the technical field of speech emotion recognition, in particular, the invention relates to a feature extraction method applied to a speech emotion recognition system. Background technique [0002] With the rapid development of computer network communication technology and multimedia technology, new human-computer interaction technology has become a very active research topic in the field of computer science. The research of speech emotion recognition has important practical significance for enhancing the intelligence and humanization of computers, developing new man-machine environments, and promoting the development of psychology and other disciplines, and will eventually produce great economic and social benefits. Speech emotion recognition is also very beneficial to the improvement of the overall technology of speech recognition. Generally speaking, speech contains rich information such as speech content, speaker, emotion, ...

AI Technical Summary

Problems solved by technology

This difference shows that the commonly used feature MFCC based on the uniform Mayer domain cannot meet the requirements of emotion recognition, and there is an urgent need for a feature extraction method that is more suitable for speech emotion recognition.

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