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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI | http://purl.tuc.gr/dl/dias/41612105-128F-4174-8DF7-F2E148F004D9 | - |
| Identifier | https://www.researchgate.net/publication/221488645_Adaptation_of_hidden_Markov_models_using_multiple_stochastic_transformations | - |
| Language | en | - |
| Title | Adaptation of hidden markov models using multiple stochastic transformations | en |
| Creator | Diakoloukas Vasilis | en |
| Creator | Διακολουκας Βασιλeioς | el |
| Creator | Digalakis Vasilis | en |
| Creator | Διγαλακης Βασιλης | el |
| Content Summary | The recognition accuracy in recent large vocabulary Automatic Speech Recognition (ASR) systems is highly related to the existing mismatch between the training and test sets. For example, dialect differences across the training and testing speakers result to a significant degradation in recognition performance. Some popular adaptation approaches improve the recognition performance of speech recognizers based on hidden Markov models with continuous mixture densities by using linear transforms to adapt the means, and possibly the covariances of the mixture Gaussians. In this paper, we propose a novel adaptation technique that adapts the means and, optionally, the covariances of the mixture Gaussians by using multiple stochastic transformations. We perform both speaker and dialect adaptation experiments, and we show that our method significantly improves the recognition accuracy and the robustness of our system. The experiments are carried out with SRI's DECIPHER TM speech recognition. | en |
| Type of Item | Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Publication | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-08 | - |
| Date of Publication | 1997 | - |
| Subject | HMMs (Hidden Markov models) | en |
| Subject | hidden markov models | en |
| Subject | hmms hidden markov models | en |
| Bibliographic Citation | V. Diakoloukas and V. V. Digalakis, "Adaptation of hidden Markov models using multiple stochastic transformations," presented at Fifth European Conference on Speech Communication and Technology, Rhodes, Greece, 1997. | en |
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