Abstract
A new duration intrinsic model for improved speech recognition by HMM techniques is presented. Assuming an exponentially decaying time dependency of the states loop probability, the duration density can be factorized and a path early pruning theorem demonstrated. As a consequence, computational complexity is greatly reduced with respect to explicit models, whereas recognition performances improve considerably.
This work has been partially founded by ALCATEL-FACE. Only the author is responsible for the ideas and conclusions here reported.
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© 1992 Springer-Verlag Berlin Heidelberg
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Falaschi, A. (1992). Continuously Variable Transition Probability HMM for Speech Recognition. In: Laface, P., De Mori, R. (eds) Speech Recognition and Understanding. NATO ASI Series, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76626-8_14
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DOI: https://doi.org/10.1007/978-3-642-76626-8_14
Publisher Name: Springer, Berlin, Heidelberg
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