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Emotional speaker identification method based on reliability detection of fuzzy support vector machine

A fuzzy support vector, speaker recognition technology, applied in the field of emotional speaker recognition, can solve the problem of speaker recognition system performance degradation, inconsistency, etc., to achieve the effect of improving performance and improving robustness

Inactive Publication Date: 2011-09-28
ZHEJIANG UNIV
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Problems solved by technology

However, traditional speaker recognition systems cannot deal with this mismatch of training and testing conditions. Therefore, emotional speaker recognition needs to solve the performance degradation of the speaker recognition system caused by the emotional inconsistency of the speaker during the training and testing stages. The problem

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  • Emotional speaker identification method based on reliability detection of fuzzy support vector machine
  • Emotional speaker identification method based on reliability detection of fuzzy support vector machine
  • Emotional speaker identification method based on reliability detection of fuzzy support vector machine

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Embodiment Construction

[0039] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0040] Such as figure 1 As shown, the emotional speaker recognition method based on reliability detection of fuzzy support vector machine mainly includes four steps

[0041] 1) Extract the speech component features and combine them with the corresponding weights in the UBM model to form the general background model component features;

[0042] 2) Use the general background model component features obtained in the step 1) as the fuzzy membership degree, and establish the fuzzy support vector machine model UCFSVM under the general background model component;

[0043] 3) Carry out reliability detection pass score for the fuzzy support vector machine model UCFSVM in step 2) Reliable features are obtained by judging the size of

[0044] 4) Perform calculations on the reliable features in step 3) to identify the speaker.

[0045] Common backgrou...

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Abstract

The invention discloses an emotional speaker identification method based on reliability detection of fuzzy support vector machine, which comprises the following steps of: extracting speech component characteristics and combining the speech component characteristics with corresponding weight in a universal broadcast modem (UBM) model to form background model component characteristics; taking the obtained background model component characteristics as a fuzzy membership, and establishing a fuzzy support component machine model in a general model component; carrying out a reliability detection byusing the fuzzy support vector machine model to obtain the reliability characteristics; and calculating the reliability characteristics and identifying the speaker. The method provided by the invention improves the robustness of the speaker identification system and the performance for identifying a speaker.

Description

technical field [0001] The invention relates to signal processing and pattern recognition, in particular to an emotional speaker recognition method based on fuzzy support vector machine reliability feature detection. Background technique [0002] Speaker recognition technology refers to the technology of using signal processing and pattern recognition methods to identify the speaker's identity according to the speaker's voice. It mainly includes two steps: speaker model training and voice testing. [0003] Currently, the main features used in speaker recognition include Mel cepstral coefficients ( ), linear predictive coding cepstral coefficients ( ), the sensory weighted linear predictive coefficient ( ). The algorithm of speaker recognition mainly includes vector quantization ( ), the generic background model approach ( ),Support Vector Machines( )etc. in, It is widely used in the field of speaker recognition. [0004] In emotional speaker recognition, the ...

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Application Information

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IPC IPC(8): G10L17/00G10L17/02G10L17/04G10L17/14G10L25/63
Inventor 杨莹春陈力吴朝晖
Owner ZHEJIANG UNIV
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