A Probabilistic Measure of Modality Reliability in Speaker Verification

In this paper, a probabilistic measure for reliability of speaker verification under noisy acoustic conditions is proposed. A Bayesian network is used to estimate a probability for verification errors, given the GMM-based speaker verification system output and additional information about the level of acoustic noise. In particular, the log-likelihood ratio and a signal-to-noise related feature are used to account for the adverse acoustic conditions. The probabilistic measure is subsequently employed in governing a repair sequence of trials for acquiring additional speech presentations which are less likely to lead to unreliable verification. The potential of the proposed method is tested through cross-validation experiments. Finally, the benefits of the repair sequence in terms of verification accuracy is evaluated on a noisy environment speaker verification task.


Published in:
Proceedings of the IEEE International Conf. on Acoustics, Speech and Signal Processing 2005 (ICASSP 2005), I, 709-712
Presented at:
IEEE International Conf. on Acoustics, Speech and Signal Processing 2005 (ICASSP 2005), Philadelphia, March 19-23, 2005
Year:
2005
Publisher:
Philadelphia, IEEE
Note:
Best student paper award in the speech processing category
Laboratories:




 Record created 2009-10-23, last modified 2018-03-17


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