Improved approach for calculating model parameters in speaker recognition using Gaussian mixture models

Prashant Metkar, Aaron Cohen, Keshab Parhi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In speaker identification, most of the computation is due to the distance or likelihood calculation between feature vectors of the test signal and the speaker model in the database. The time required for identifying a speaker is a function of feature vectors and their dimensionality and the number of speakers in the database. In this paper, we focus on optimizing the performance of Gaussian mixture (GMM) based speaker identification system. An improved approach for model parameter calculation is presented. The advantage of proposed approach lies in the reduction in computational time by a significant amount over an approach which uses expectation maximization (EM) algorithm to calculate the model parameter values. This approach is based on forming clusters and assigning weights to them depending upon the number of mixtures used for modeling the speaker. The reduction in computation time depends upon how many mixtures are used for training the speaker model.

Original languageEnglish (US)
Title of host publicationConference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Pages567-570
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
CountryUnited States
CityPacific Grove, CA
Period11/7/1011/10/10

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