RASTA channel compensation for speaker verification system

Pao Yue-kong Library Electronic Theses Database

RASTA channel compensation for speaker verification system

 

Author: Yuen, Chi-leung
Title: RASTA channel compensation for speaker verification system
Degree: M.Sc.
Year: 1999
Subject: Speech processing systems,
Automatic speech recognition
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Electronic Engineering
Pages: 83 leaves : ill. ; 31 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1485405
URI: http://theses.lib.polyu.edu.hk/handle/200/4624
Abstract: The environmental factors tend to degrade the performance of speaker verification systems. The degree of degradation varies with the speech analysis methods, the channel compensation methods and the verification techniques. By using the YOHO corpus [4] and Elliptical Basis Function (EBF) neural networks [1] [2] [3], the robustness and reliability of three different feature extraction methods were evaluated. The features include (a) Perceptual Linear Predictive (PLP) coefficients [6], (b) LP-derived Cepstrum (CEP) coefficients [7], and (c) Relative Spectral-Perceptual Linear Predictive (RASTA-PLP) coefficients [5]. Experimental results show that the performance of the PLP method is superior to the other two methods. For example, the PLP method achieves a verification error rate of 14.9%, whereas the error rates are 22.2% and 27.5% for methods using the RASTA-PLP coefficients and the CEP coefficients, respectively. Results also demonstrate that optimal EBF network parameters for clean speech does not necessarily lead to optimal performance for channel distorted speech. For different prediction orders, a separated set of network parameters is essential for achieving optimal verification performance. Lastly, this study finds that the best Equal Error Rate (EER) does not bring about the best verification performance for a speaker recognition system. However, the EER can be used to measure the degree of overlapping among feature clusters and to compare the decision boundaries' effectiveness in separating speaker features.

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