NDLI: A New Feature Transformation Method Based on Rotation for Speaker Identification

Content Provider	IEEE Xplore Digital Library
Author	Min-Seok Kim Ha-Jin Yu
Copyright Year	2007
Description	Author affiliation: Univ. of Seoul, Seoul (Min-Seok Kim; Ha-Jin Yu)
Abstract	In this paper, we propose a new feature transformation method that is optimized for diagonal covariance Gaussian mixture models which is used for a speaker identification system. We first define an object function as the distances between the Gaussian mixture components and rotate each plane in the feature space to maximize the object function. The optimal degrees of the rotations are found using the particle swarm optimization algorithm. We applied the transformation to a speaker identification task in unknown noisy environments. The proposed transformation is compared with conventional principle component analysis and linear discriminant analysis. The results show that the proposed feature transformation method outperformed existing methods in very high noise environment.
Starting Page	68
Ending Page	73
File Size	511085
Page Count	6
File Format	PDF
ISBN	9780769530154
ISSN	10823409
DOI	10.1109/ICTAI.2007.49
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-29
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Working environment noise Linear discriminant analysis Speaker recognition Particle swarm optimization Feature extraction Optimization methods Vectors Principal component analysis Speech processing Cepstral analysis
Content Type	Text
Resource Type	Article

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