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Robust Speaker Verification From GSM-Transcoded Speech Based On Decision Fusion And Feature Transformation (2003)
| Venue: | in Proc. IEEE ICASSP’03 |
| Citations: | 9 - 6 self |
BibTeX
@INPROCEEDINGS{Mak03robustspeaker,
author = {Man-Wai Mak and Ming-cheung Cheung and Sun-Yuan Kung},
title = {Robust Speaker Verification From GSM-Transcoded Speech Based On Decision Fusion And Feature Transformation},
booktitle = {in Proc. IEEE ICASSP’03},
year = {2003},
pages = {745--748}
}
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Abstract
In speaker verification, a claimant may produce two or more utterances. Typically, the scores of the speech patterns extracted from these utterances are averaged and the resulting mean score is compared with a decision threshold. Rather than simply computing the mean score, we propose to compute the optimal weights for fusing the scores based on the score distribution of the independent utterances and our prior knowledge about the score statistics. More specifically, we use enrollment data to compute the mean scores of client speakers and impostors and consider them to be the prior scores. During verification, we set the fusion weights for individual speech patterns to be a function of the dispersion between the scores of these speech patterns and the prior scores. Experimental results based on the GSM-transcoded speech of 150 speakers from the HTIMIT corpus demonstrate that the proposed fusion algorithm can increase the dispersion between the mean speaker scores and the mean impostor scores. Compared with a baseline approach where equal weights are assigned to all scores, the proposed approach provides a relative error reduction of 19%.
Keyphrases
gsm-transcoded speech feature transformation decision fusion robust speaker verification mean score speech pattern prior score fusion weight baseline approach decision threshold independent utterance equal weight score distribution optimal weight speaker verification fusion algorithm client speaker individual speech pattern htimit corpus demonstrate mean speaker score enrollment data prior knowledge mean impostor score score statistic relative error reduction experimental result
