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

<p/> <p>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 imp...

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Main Authors: Lin Jie, Ming Ji, Smith F Jack
Format: Article
Language:English
Published: SpringerOpen 2006-01-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://dx.doi.org/10.1155/ASP/2006/75390
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author Lin Jie
Ming Ji
Smith F Jack
author_facet Lin Jie
Ming Ji
Smith F Jack
author_sort Lin Jie
collection DOAJ
description <p/> <p>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.</p>
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spelling doaj.art-291177b591414a81a31af6c3fda1377e2022-12-21T18:48:15ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802006-01-0120061075390A Posterior Union Model with Applications to Robust Speech and Speaker RecognitionLin JieMing JiSmith F Jack<p/> <p>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.</p>http://dx.doi.org/10.1155/ASP/2006/75390
spellingShingle Lin Jie
Ming Ji
Smith F Jack
A Posterior Union Model with Applications to Robust Speech and Speaker Recognition
EURASIP Journal on Advances in Signal Processing
title A Posterior Union Model with Applications to Robust Speech and Speaker Recognition
title_full A Posterior Union Model with Applications to Robust Speech and Speaker Recognition
title_fullStr A Posterior Union Model with Applications to Robust Speech and Speaker Recognition
title_full_unstemmed A Posterior Union Model with Applications to Robust Speech and Speaker Recognition
title_short A Posterior Union Model with Applications to Robust Speech and Speaker Recognition
title_sort posterior union model with applications to robust speech and speaker recognition
url http://dx.doi.org/10.1155/ASP/2006/75390
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AT smithfjack aposteriorunionmodelwithapplicationstorobustspeechandspeakerrecognition
AT linjie posteriorunionmodelwithapplicationstorobustspeechandspeakerrecognition
AT mingji posteriorunionmodelwithapplicationstorobustspeechandspeakerrecognition
AT smithfjack posteriorunionmodelwithapplicationstorobustspeechandspeakerrecognition