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Training issues and channel equalization techniques for the construction of telephone acoustic models using a high-quality speech corpus

Neumeyer Leonardo, Digalakis Vasilis, Weintraub M.

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URI: http://purl.tuc.gr/dl/dias/A626F192-FFCC-4C9F-B53E-E0F41DB33825
Έτος 1994
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
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Βιβλιογραφική Αναφορά L. Neumeyer, V. Digalakis and M. Weintraub, "Training issues and channel equalization techniques for the construction of telephone acoustic models using a high-quality speech corpus," IEEE Trans. Speech Audio Process., vol. 2, no. 4, pp. 590-597, Oct. 1994. doi:10.1109/89.326617 https://doi.org/10.1109/89.326617
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Περίληψη

We describe an approach for the estimation of acoustic phonetic models that will be used in a hidden Markov model (HMM) recognizer operating over the telephone. We explore two complementary techniques to developing telephone acoustic models. The first technique presents two new channel compensation algorithms. Experimental results on the Wall Street Journal corpus show no significant improvement over sentence-based cepstral-mean removal. The second technique uses an existing “high-quality” speech corpus to train acoustic models that are appropriate for the switchboard credit card task over long-distance telephone lines. Experimental results show that cross-database acoustic training yields performance similar to that of conventional task-dependent acoustic training

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