NDLI: Grid-density based feature classification for speaker recognition

Content Provider	IEEE Xplore Digital Library
Author	Lin Li Wei Wang Shan He
Copyright Year	2012
Description	Author affiliation: Department of Electronic Engineering, Xiamen University Xiamen, China (Lin Li; Wei Wang; Shan He)
Abstract	A new strategy of feature classification method for speaker recognition based on the grid-density clustering is presented. According to the concept of density-based and grid-distance-based distribution in the Mel-frequency cepstrum domain, the feature vectors of each speaker were self-adaptively classified into L clusters with less overlapped. With these convex and non-interwoven clusters, the Gaussian Mixture Model could statistically analyze and estimate the distinct feature classification for each speaker. Moreover, a new speaker recognition system was established by using GMM-UBM model. The experimental results showed that the clustering effect of the proposed method was superior to the K-means plus EM clustering method, and the proposed speaker recognition system achieves better classification performance in terms of verification accuracy and computational complexity.
Starting Page	1
Ending Page	4
File Size	395737
Page Count	4
File Format	PDF
ISBN	9781467321440
ISSN	21635048
e-ISBN	9781467321457
e-ISBN	9781467321433
DOI	10.1109/ICASID.2012.6325282
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	grid-density based clustering Accuracy Clustering methods Computational modeling Clustering algorithms Signal processing algorithms Vectors Speaker recognition feature classification speaker recogniton
Content Type	Text
Resource Type	Article

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