NDLI: Skew Gaussian mixture models for speaker recognition

Content Provider	IET Digital Library
Author	Matza, Avi Bistritz, Yuval
Abstract	Gaussian mixture models (GMMs) are widely used in speech and speaker recognition. This study explores the idea that a mixture of skew Gaussians might capture better feature vectors that tend to have skew empirical distributions. It begins with deriving an expectation maximisation (EM) algorithm to train a mixture of two-piece skew Gaussians that turns out to be not much more complicated than the usual EM algorithm used to train symmetric GMMs. Next, the algorithm is used to compare skew and symmetric GMMs in some simple speaker recognition experiments that use Mel frequency cepstral coefficients (MFCC) and line spectral frequencies (LSF) as the feature vectors. MFCC are one of the most popular feature vectors in speech and speaker recognition applications. LSF were chosen because they exhibit significantly more skewed distribution than MFCC and because they are widely used [together with the related immittance spectral frequencies (ISF)] in speech transmission standards. In the reported experiments, models with skew Gaussians performed better than models with symmetric Gaussians and skew GMMs with LSF compared favourably with both skew symmetric and symmetric GMMs that used MFCC.
Starting Page	860
Ending Page	867
Page Count	8
ISSN	17519675
Volume Number	8
e-ISSN	17519683
Issue Number	Issue 8, Oct (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/8/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0270
Journal	IET Signal Processing
Publisher Date	2014-10-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	EM Algorithm Expectation Maximisation Algorithm Expectation-maximisation Algorithm Feature Vector Gaussian Processes GMM Immittance Spectral Frequency Interpolation And Function Approximation ISF Line Spectral Frequency Linear Algebra LSF Mel Frequency Cepstral Coefflcient MFCC Mixture Model Numerical Analysis Skew Empirical Distribution Speaker Recognition Speech Processing Technique Speech Recognition Speech Recognition And Synthesis Speech Transmission Standard Statistics Two-piece Skew Gaussian Mixture Model Vector
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Text dependent speaker recognition using shifted MFCC

Text dependent speaker recognition using shifted MFCC

New technique to use the GMM in speaker recognition system (SRS)

Speech recognizer for regional languages of Pakistan

An analysis and comparative evaluation of MFCC variants for speaker identification over VoIP networks

Speaker Identification and Verification by Combining MFCC and Phase Information

Emotion recognition in speech using MFCC and wavelet features

Implementation of secure speaker verification at web login page using Mel Frequency Cepstral Coefficient-Gaussian Mixture Model (MFCC-GMM)

Age and gender recognition of a speaker from short-duration phone conversations

Skew Gaussian mixture models for speaker recognition

Similar Documents

Text dependent speaker recognition using shifted MFCC

Text dependent speaker recognition using shifted MFCC

New technique to use the GMM in speaker recognition system (SRS)

Speech recognizer for regional languages of Pakistan

An analysis and comparative evaluation of MFCC variants for speaker identification over VoIP networks

Speaker Identification and Verification by Combining MFCC and Phase Information

Emotion recognition in speech using MFCC and wavelet features

Implementation of secure speaker verification at web login page using Mel Frequency Cepstral Coefficient-Gaussian Mixture Model (MFCC-GMM)

Age and gender recognition of a speaker from short-duration phone conversations

Skew Gaussian mixture models for speaker recognition