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A Posterior Union Model with Applications to Robust Speech and Speaker Recognition

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 075390 (2006) Cite this article

Abstract

This paper investigates speech and speaker recognition involving partial feature corruption, assuming unknown, time-varying noise characteristics. The probabilistic union model is extended from a conditional-probability formulation to a posterior-probability formulation as an improved solution to the problem. The new formulation allows the order of the model to be optimized for every single frame, thereby enhancing the capability of the model for dealing with nonstationary noise corruption. The new formulation also allows the model to be readily incorporated into a Gaussian mixture model (GMM) for speaker recognition. Experiments have been conducted on two databases: TIDIGITS and SPIDRE, for speech recognition and speaker identification. Both databases are subject to unknown, time-varying band-selective corruption. The results have demonstrated the improved robustness for the new model.

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Authors and Affiliations

  1. School of Computer Science, Queen's University Belfast, Belfast, BT7 1NN, United Kingdom

    Ji Ming & F. Jack Smith

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Jie Lin

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  1. Ji Ming
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  2. Jie Lin
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Correspondence to Ji Ming.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Ming, J., Lin, J. & Smith, F.J. A Posterior Union Model with Applications to Robust Speech and Speaker Recognition. EURASIP J. Adv. Signal Process. 2006, 075390 (2006). https://doi.org/10.1155/ASP/2006/75390

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  • DOI: https://doi.org/10.1155/ASP/2006/75390

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