Abstract
In speech recognition huge hypothesis spaces are generated. To overcome this problem dynamic programming can be used. In this paper we examine ways of speeding up this search process even more using heuristic search methods, multi-pass search and aggregation operators. The tests showed that these techniques can be applied together, and their combination could significantly speed up the recognition process. The run-times we obtained were 22 times faster than the basic dynamic search method, and 8 times faster than the multi-stack decoding method.
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Gosztolya, G., Kocsor, A.: Aggregation Operators and Hypothesis Space Reductions in Speech Recognition. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, pp. 315–322. Springer, Heidelberg (2004)
Gosztolya, G., Kocsor, A.: A Hierarchical Evaluation Methodology in Speech Recognition. Submitted to Acta Cybernetica (2004)
Young, S., et al.: The HMM Toolkit (HTK) (software and manual), http://htk.eng.cam.ac.uk/
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© 2005 Springer-Verlag Berlin Heidelberg
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Gosztolya, G., Kocsor, A. (2005). Speeding Up Dynamic Search Methods in Speech Recognition. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_16
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DOI: https://doi.org/10.1007/11504894_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
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