Speech emotion recognition method based on fuzzy support vector machine

Abstract

The invention relates to a speech emotion recognition technology, in particular to a speech emotion recognition method based on a fuzzy support vector machine. The method comprises the steps that input speech signals with emotions are pre-processed, the pre-processing comprises pre-emphasis filtering and windowing framing, the Mel frequency cepstrum coefficient (MFCC) of feature information of the processed speech signals is extracted, dimension reduction processing is carried out on the extracted MFCC by utilizing principal component analysis (KPCA), classification and recognition are carried out according to the MFCC feature information after the dimension reduction is carried out, and recognition results are output. A specific classification and recognition method is carried out by adopting an FSVM algorithm. The speech emotion recognition method based on the fuzzy support vector machine has the advantages that KPCA is adopted for carrying out dimension reduction on MFCC emotion features to reduce redundant information, the recognition effect is better than that with the MFCC features directly used, recognition efficiency of the method is higher, the effect is better, and the recognition speed is higher. The speech emotion recognition method based on the fuzzy support vector machine is especially suitable for intelligent speech emotion recognition.

Description

Technical field [0001] The invention relates to speech emotion recognition technology, in particular to a speech emotion recognition method based on fuzzy support vector machines. Background technique [0002] Experts have been studying emotions in the fields of physiology and psychology for a long time. With the rapid development of artificial intelligence, the study of emotions in human-computer interaction has aroused great interest among experts. In human-computer interaction, it is hoped that humans and machines can communicate more naturally, which requires machines to understand human emotions, so the classification and recognition of emotions by machines is particularly important. In human communication, speech contains a wealth of information, so machines can classify and recognize emotions through speech. Experts have conducted a lot of research and analysis on speech emotion classification and recognition, generally including the establishment of speech emotion databa...

AI Technical Summary

Problems solved by technology

The predecessors used the Mel cepstral coefficient (MFCC) as the recognition feature, but this feature was not further processed before the recognition, which would cause a lot of redundant information to affect the recognition effect

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