NDLI: Speaker Recognition Using Spectral Dimension Features

Content Provider	IEEE Xplore Digital Library
Author	Wen-Shiung Chen Jr-Feng Huang
Copyright Year	2009
Abstract	Biometric recognition is more and more important due to security applications all over the world. Mobile phone becomes popular in recent years. Therefore voice recognition for recognizing a speaker’s identity also plays a potential role. This paper presents a speaker recognition that combines a non-linear feature, named spectral dimension (SD), with Mel Frequency Cepstral Coefficients (MFCC). In order to improve the performance of the proposed scheme, the Mel-scale method is adopted for allocating sub-bands and the pattern matching is trained by Gaussian mixture model. Some problems related to spectral dimension are discussed and the comparison with other simple spectral features is made. We observe that our proposed methods can improve the performance in different components. For instance, speaker verification combining MFCC with our proposed SD features gives a good performance of EER=2.3140% by 32_Multi-GMM. The relative improvement is about 22% better than the method that is based only on MFCC with EER=2.9631%.
Starting Page	132
Ending Page	137
File Size	757392
Page Count	6
File Format	PDF
ISBN	9781424446803
DOI	10.1109/ICCGI.2009.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-23
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biometrics Fractal Dimension Spectral Dimension Fractals Speaker recognition Mel frequency cepstral coefficient Information technology Biometric Recognition Automatic speech recognition Speaker Recognition Information security Speech recognition Feature extraction Biomedical signal processing
Content Type	Text
Resource Type	Article

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