Speech emotion recognition method based on fuzzy support vector machine

A fuzzy support vector, speech emotion recognition technology, applied in speech recognition, speech analysis, instruments, etc., can solve the problem of information affecting the recognition effect, etc., to achieve high recognition rate, good effect, and reduce noise or isolated points.

Inactive Publication Date: 2014-10-08
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

The predecessors used the Mel cepstral coefficient (MFCC) as the recognition feature, but this feature was not furthe

Method used

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  • Speech emotion recognition method based on fuzzy support vector machine
  • Speech emotion recognition method based on fuzzy support vector machine
  • Speech emotion recognition method based on fuzzy support vector machine

Examples

Experimental program
Comparison scheme
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Embodiment

[0030] In this example, based on the Berlin Speech Emotion Database (Emo-DB), four types of emotions: happy, angry, sad, and calm are selected for speech emotion recognition. The emotional speech is divided into two groups, one group is used as the training sample for classification, and the other is the test sample for recognition .

[0031] Such as figure 1 As shown, this example includes the following steps:

[0032] S1: Pretreatment

[0033] Pre-processing includes pre-emphasis filtering and windowing and framing.

[0034] Pre-emphasis filter processing: The purpose of pre-emphasis is to flatten the frequency spectrum of the signal, maintain the entire frequency band from low frequency to high frequency, and use the same signal-to-noise ratio to find the frequency spectrum, so as to facilitate spectrum analysis or channel parameter analysis. Pre-emphasis generally uses a first-order digital filter H(z) = 1-αz -1 , Where α is the pre-emphasis coefficient, in this example α is 0....

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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...

Claims

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Application Information

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IPC IPC(8): G10L25/63G10L17/02G10L15/06G10L17/04
Inventor 周代英谭发曾贾继超田兵兵寥阔
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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