NDLI: A Preliminary Study on GMM Weight Transformation for Emotional Speaker Recognition

Content Provider	IEEE Xplore Digital Library
Author	Li Chen Yingchun Yang
Copyright Year	2013
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China (Li Chen; Yingchun Yang)
Abstract	The performance of speaker recognition system degrades when the emotional states are inconsistent during the enrollment and evaluation stage. Emotional GMM model synthesis, such as NEGT (Neutral-Emotional GMM mean Transformation), is one way to reduce this degradation. This paper discovers that GMM weight transformation is also feasible and the number of parameters that need to be modified is much less than that of GMM mean ransformation. Thus, we propose two algorithms: RBFNN (Radial Basis Function Neural Network) and EBSR (Exemplar Based Sparse Representation) based GMM weight transformation to model the neutral-to-emotion weight transformation law for emotional GMM model synthesis. The experiments carried on MASC show that IR has been increased by 6.91% and 5.74% through these two algorithms respectively, compared with that of the GMM-UBM system. Meanwhile, these two algorithms require less development data and time compared with those of NEGT.
Starting Page	31
Ending Page	36
File Size	274214
Page Count	6
File Format	PDF
ISBN	9780769550480
ISSN	21568111
DOI	10.1109/ACII.2013.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-02
Publisher Place	Switzerland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Neural Network Signal processing algorithms Emotional Speaker Recognition Sparse Representation Speech Acoustics Vectors Robustness Speaker recognition
Content Type	Text
Resource Type	Article

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