Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/00ED0C02-D3BE-44AA-B964-C0F7FE113B99 | - |
| Αναγνωριστικό | http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=466659&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel4%2F89%2F9789%2F00466659.pdf%3Farnumber%3D466659 | - |
| Αναγνωριστικό | https://doi.org/10.1109/89.466659 | - |
| Γλώσσα | en | - |
| Μέγεθος | 10 pages | en |
| Τίτλος | Speaker adaptation using constrained estimation of Gaussian mixtures | en |
| Δημιουργός | Digalakis Vasilis | en |
| Δημιουργός | Διγαλακης Βασιλης | el |
| Δημιουργός | Rtischev D. | en |
| Δημιουργός | Neumeyer Leonardo | en |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | A trend in automatic speech recognition systems is the use of continuous mixture-density hidden Markov models (HMMs). Despite the good recognition performance that these systems achieve on average in large vocabulary applications, there is a large variability in performance across speakers. Performance degrades dramatically when the user is radically different from the training population. A popular technique that can improve the performance and robustness of a speech recognition system is adapting speech models to the speaker, and more generally to the channel and the task. In continuous mixture-density HMMs the number of component densities is typically very large, and it may not be feasible to acquire a sufficient amount of adaptation data for robust maximum-likelihood estimates. To solve this problem, the authors propose a constrained estimation technique for Gaussian mixture densities. The algorithm is evaluated on the large-vocabulary Wall Street Journal corpus for both native and nonnative speakers of American English. For nonnative speakers, the recognition error rate is approximately halved with only a small amount of adaptation data, and it approaches the speaker-independent accuracy achieved for native speakers. For native speakers, the recognition performance after adaptation improves to the accuracy of speaker-dependent systems that use six times as much training data | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-11-02 | - |
| Ημερομηνία Δημοσίευσης | 1995 | - |
| Θεματική Κατηγορία | HMM | en |
| Θεματική Κατηγορία | Hidden Markov Models | en |
| Θεματική Κατηγορία | Speech recognition | en |
| Βιβλιογραφική Αναφορά | V. Digalakis, D. Rtischev and L. Neumeyer, "Speaker adaptation using constrained estimation of Gaussian mixtures," IEEE Trans. Speech Audio Process., vol. 3, no. 5, pp. 357-366, Sep. 1995. doi:10.1109/89.466659 | en |
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