In speaker identification, most of the computational processing time is required to calculate the likelihood of the test utterance of the unknown speaker with respect to the speaker models in the database. When number of speakers in the database is in the order of 10,000 or more, then computational complexity becomes very high. In this paper, we propose a Maximum Likelihood Linear Regression (MLLR) based fast method to calculate the likelihood from the speaker model using the MLLR matrix. The proposed technique will help to quickly find the best N speakers during identification. After that final speaker identification task can be done within the N selected speakers using any conventional method of speaker identification. The comparative study of the proposed method is done in terms of processing time with the state-of-the-art GMM-UBM based system on NIST 2004 SRE. The proposed technique performs faster than GMM-UBM based system with some degradation in system accuracy. ©2010 IEEE.

Srinivasan Umesh

Department of Electrical Engineering

Comparative studies

COMPUTATIONAL PROCESSING TIME

Conventional methods

Fast methods

Large population

matrix

MAXIMUM LIKELIHOOD LINEAR REGRESSION

Processing time

SPEAKER IDENTIFICATION

SPEAKER MODEL

SUFFICIENT STATISTICS

SYSTEM ACCURACY

Computational complexity

LOUDSPEAKERS

MAGNETOSTRICTIVE DEVICES

Maximum likelihood

Population statistics

Speech Recognition

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IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Fast approach to speaker identification for large population using MLLR and sufficient statistics

Proceedings of 16th National Conference on Communications, NCC 2010

In this paper, we propose a computationally efficient method to identify a speaker from a large population of speakers. The proposed method is based on our earlier [1] Fast Maximum Likelihood Linear Linear Regression (MLLR) anchor modeling technique which provides performance comparable to the conventional anchor modeling system and yet reduces computation time significantly by computing likelihood efficiently using sufficient statistics of data and anchor specific MLLR matrix. However, both these systems still require a Gaussian Mixture Model-Universal Background Model (GMM-UBM) based back-end system to choose the optimal speaker, which is computationally heavy. In our proposed method, we show that applying Linear-Discriminant Analysis (LDA) and Within-Class-Covariance Normalization (WCCN) on the Speaker characterization Vector (SCV) of our recently proposed Fast-MLLR method, we can combine the computational efficiency and the discriminant capability to have a system that uses simple cosine-distance measure to identify speakers and yet has significantly superior performance compared to both full-blown GMM-UBM system and the anchor-model system. More importantly, there is no need of the "back-end" system. Experimental result on NIST 2004 SRE shows that the proposed method reduces identification error rate by an absolute 2% and takes only 2/3 of the time taken by efficient Fast-MLLR system and only 20% of the time taken by the stand-alone GMM-UBM system. © 2012 IEEE.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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2006 IEEE Odyssey - The Speaker and Language Recognition Workshop

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IEEE Transactions on Audio, Speech and Language Processing

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Journal	Proceedings of 16th National Conference on Communications, NCC 2010
Open Access	No