Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Article

Source Title

International Journal of Advanced Trends in Computer Science and Engineering

Volume

9

Issue

1

Publication Date

1-1-2020

Abstract

This paper describes a speaker recognition system based on feature extraction utilizing the constrained maximum likelihood linear regression (CMLLR) speaker adaptation, while using Gaussian mixture models (GMM) to model the speaker and background models. For the input acoustic signals, the cepstral features are derived to highlight the differences between test and training utterances. The CLSU dataset is used to test the efficiency and performance of the proposed CMLLR, Support Vector Machine, and GMM methods for modeling the speaker’s voice by characterizing the speaker features. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

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Digitial Object Identifier (DOI)

10.30534/ijatcse/2020/77912020

Disciplines

Electrical and Computer Engineering

Keywords

Automatic speech recognition; Regression analysis

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