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...
Main Authors: | , , |
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Format: | Article |
Language: | English |
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SpringerOpen
2006-01-01
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/ASP/2006/75390 |
_version_ | 1819090492148678656 |
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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> |
first_indexed | 2024-12-21T22:24:41Z |
format | Article |
id | doaj.art-291177b591414a81a31af6c3fda1377e |
institution | Directory Open Access Journal |
issn | 1687-6172 1687-6180 |
language | English |
last_indexed | 2024-12-21T22:24:41Z |
publishDate | 2006-01-01 |
publisher | SpringerOpen |
record_format | Article |
series | EURASIP Journal on Advances in Signal Processing |
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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