Advisor

Prasad, Saurabh

Committee Member

Fowler, James

Committee Member

Baca, Julie

Committee Member

Miller, Len T.

Date of Degree

1-1-2011

Document Type

Dissertation - Open Access

Major

Electrical and Computer Engineering

Degree Name

Doctor of Philosophy

Department

Department of Electrical and Computer Engineering

Abstract

In this work, we apply a nonlinear mixture autoregressive (MixAR) model to supplant the Gaussian mixture model for speaker verification. MixAR is a statistical model that is a probabilistically weighted combination of components, each of which is an autoregressive filter in addition to a mean. The probabilistic mixing and the datadependent weights are responsible for the nonlinear nature of the model. Our experiments with synthetic as well as real speech data from standard speech corpora show that MixAR model outperforms GMM, especially under unseen noisy conditions. Moreover, MixAR did not require delta features and used 2.5x fewer parameters to achieve comparable or better performance as that of GMM using static as well as delta features. Also, MixAR suffered less from overitting issues than GMM when training data was sparse. However, MixAR performance deteriorated more quickly than that of GMM when evaluation data duration was reduced. This could pose limitations on the required minimum amount of evaluation data when using MixAR model for speaker verification.

URI

https://hdl.handle.net/11668/17275

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