Voice emotion recognition method

Abstract

The invention relates to a voice emotion recognition method, which comprises the following steps of obtaining a plurality of voice signals from a voice database; combining a plurality of voice features, a plurality of nonlinear features and a plurality of macroscopical features into a plurality of groups of feature combinations; and extracting the voice features, the nonlinear features and the macroscopical features of each first voice signal in the voice signals so as to obtain a plurality of calculated features. The method also comprises the following steps of selecting a plurality of target feature quantities from the calculated features; using the target feature quantities to train one voice emotion classification model so as to obtain a plurality of recognition rates corresponding to the feature combinations; and selecting a plurality of target feature combinations from the feature combinations according to the recognition rate. The method also comprises the step of executing the voice emotion recognition operation according to the target feature combinations and the voice emotion classification model.

Description

technical field [0001] The present invention relates to a speech emotion recognition method, and particularly relates to a method for establishing a speech emotion classification model, a method for identifying emotion corresponding to a received speech signal by using the established speech emotion classification model, and using the method speech emotion recognition system. Background technique [0002] In recent years, due to the rapid development of artificial intelligence, machine learning and network information, the communication mode between people and intelligent electronic devices is no longer the way of inputting commands to the electronic device in the past, and the electronic device then responds by text. can satisfy. Therefore, the control of intelligent electronic devices through the most natural and convenient "voice" communication medium will become the trend of the future. For example, there are many things that can be replaced by machines in different fi...

AI Technical Summary

Problems solved by technology

However, this traditional speech emotion recognition technology generally only analyzes emotions from the perspective of micro-sound frames, and cannot show the complete micro- and macro-emotional changes of human beings, which makes the results of speech emotion recognition easily limited by sound frames. The collected microscopic or partial speech features

Furthermore, the one-dimensional speech signal obtained from the recording cannot fully represent the three-dimensional transfer characteristics of real speech, and it is difficult for traditional speech features to completely describe the nonlinear characteristics of real speech

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