NDLI: On text-independent speaker recognition via improved Vector Quantization method

Content Provider	IEEE Xplore Digital Library
Author	Liu Ting-ting Guan Sheng-xiao
Copyright Year	2013
Description	Author affiliation: Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China (Liu Ting-ting; Guan Sheng-xiao)
Abstract	The text-independent speaker recognition system is mainly constituted of three functional modules including speech pretreatment, the feature parameter extraction, and pattern matching judgment. The paper uses the MATLAB software to acquire a design of the system. The feature parameters utilized in this paper are Mel-Frequency Cepstrum Coefficients (MFCC) and their first-order differential characteristics. With the help of the Fisher criterion the number of the dimension of the feature parameters is decreased. Vector Quantization (VQ) model is applied to devise the optimal codebook. The paper suggests some modifications in order to improve the efficiency of the algorithm on the basis of high recognition rate: Fisher ratio of each dimensional parameter is used as weighing coefficient at the distance measurement; an approach to speeding up the search is proposed; Process the empty cell in the procedure of codebook formation. In addition, it discusses a few factors including the training and testing time, the dimension of the codebook, stopping and acceptance thresholds, which have an impact on identification accuracy rate by experimentation.
Starting Page	3912
Ending Page	3916
File Size	398954
Page Count	5
File Format	PDF
ISBN	9789881563835
ISSN	19341768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Training feature extraction vector quantization Mel-Frequency Cepstrum Coefficients (MFCC) Speech Feature extraction Vectors Speaker recognition Mel frequency cepstral coefficient Fisher criterion Testing speaker recognition
Content Type	Text
Resource Type	Article

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