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http://dx.doi.org/10.6109/jkiice.2017.21.6.1149

Speaker Independent Recognition Algorithm based on Parameter Extraction by MFCC applied Wiener Filter Method  

Choi, Jae-Seung (Division of Smart Electrical and Electronic Engineering, Silla University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.6, 2017 , pp. 1149-1154 More about this Journal
Abstract
To obtain good recognition performance of speech recognition system under background noise, it is very important to select appropriate feature parameters of speech. The feature parameter used in this paper is Mel frequency cepstral coefficient (MFCC) with the human auditory characteristics applied to Wiener filter method. That is, the feature parameter proposed in this paper is a new method to extract the parameter of clean speech signal after removing background noise. The proposed method implements the speaker recognition by inputting the proposed modified MFCC feature parameter into a multi-layer perceptron network. In this experiments, the speaker independent recognition experiments were performed using the MFCC feature parameter of the 14th order. The average recognition rates of the speaker independent in the case of the noisy speech added white noise are 94.48%, which is an effective result. Comparing the proposed method with the existing methods, the performance of the proposed speaker recognition is improved by using the modified MFCC feature parameter.
Keywords
Mel frequency cepstral coefficient; Wiener filter; speaker recognition; speaker independent; feature parameter;
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