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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Benzeghiba, M.F. Bourlard, H. |
| Copyright Year | 2003 |
| Description | Author affiliation: Dalle Molle Inst. for Perceptual Artificial Intelligence, Martigny, Switzerland (Benzeghiba, M.F.; Bourlard, H.) |
| Abstract | 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. |
| Sponsorship | IEEE Signal Process, Soc |
| File Size | 322897 |
| File Format | |
| ISBN | 0780376633 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2003.1202335 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-04-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Artificial neural networks Equations Artificial intelligence Databases Viterbi algorithm |
| Content Type | Text |
| Resource Type | Article |
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