Abstract
In this paper we describe a speech recognition system implemented with generalized dynamic Bayesian networks (dbns). We discuss the design of the system and the features of the underlying toolkit we constructed that makes efficient processing of speech and language data with Bayesian networks possible. Features include: sparse representations of probability tables, a fast algorithm for inference with probability tables, lazy evaluation of probability tables, algorithms for calculations with tree-shaped distributions, the ability to change distributions on the fly, and a generalization of dbn model structure.
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Wiggers, P., Rothkrantz, L.J.M., van de Lisdonk, R. (2010). Design and Implementation of a Bayesian Network Speech Recognizer. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_57
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DOI: https://doi.org/10.1007/978-3-642-15760-8_57
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