Hybrid HMM/ANN and GMM Combination for User-Customized Password Speaker Verification

Recently we have proposed an approach for user-customized password speaker verification; in this approach, we combined a hybrid HMM/ANN model (used for utterance verification) and a GMM model (used for speaker verification). In this paper, we extend our investigations. First, we propose a new similarity measure that uses confidence measures developed in the HMM/ANN framework. Secondly, we analyze the contribution of each model using a weighted sum combination technique. Experiments conducted on a subset of the PolyVar database show that for a short password the performance of the combined system did not improve significantly compared to the performance using the GMM model alone, and that the HMM/ANN did not contribute much in the combined system. We discuss possible reasons for this.


Published in:
Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03)
Presented at:
Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03)
Year:
2003
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Note:
IDIAP-RR 02-45
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 Record created 2006-03-10, last modified 2018-03-17

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