Abstract
In this paper we deal with the heuristic exploration of general hypothesis spaces arising both in the HMM and segment-based approaches of speech recognition. The generated hypothesis space is a tree where we assign costs to its nodes. The tree and the costs are both generated in a top-down way where we have node extension rules and aggregation operators for the cost calculation. We introduce a special set of mean aggregation operators suitable for speech recognition tasks. Then we discuss the efficiency of some heuristic search methods like the Viterbi beam search, multi-stack decoding algorithm, and some improvements using these aggregation operators. The tests showed that this technique could significantly speed up the recognition process. The run-times we obtained were 2 times faster than the basic multi-stack decoding method, and 4 times faster than the Viterbi beam search method.
This work was supported under the contract IKTA No. 2003/00056 from the Hungarian Ministry of Education.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gosztolya, G., Kocsor, A.: Improving the Multi-Stack Decoding Algorithm in a Segment-based Speech Recognizer. In: Chung, P.W.H., Hinde, C.J., Ali, M. (eds.) IEA/AIE 2003. LNCS, vol. 2718, pp. 744–749. Springer, Heidelberg (2003)
Jelinek, F.: Statistical Methods for Speech Recognition. The MIT Press, Cambridge (1997)
Rabiner, L., Juang, B.-H.: Fundamentals of Speech Recognition. Prentice Hall, Englewood Cliffs (1993)
Glass, J., Chang, J., Mccandless, M.: A Probabilistic Framework for Features-Based Speech Recognition. In: Proceedings of International Conference on Spoken Language Processing, Philadelphia, PA, pp. 2277–2280 (1996)
Tóth, L., Kocsor, A., Kovács, K.: A Discriminative Segmental Speech Model and Its Application to Hungarian Number Recognition. Text, Speech and Dialogue (2000)
Bahl, L.R., Gopalakrishnan, P.S., Mercer, R.L.: Search issues in large vocabulary speech recognition. In: Proceedings of the 1993 IEEE Workshop on Automatic Speech Recognition, Snowbird, UT (1993)
Hart, P.E., Nilsson, N.J., Raphael, B.: Correction to “A Formal Basis for the Heuristic Determination of Minimum Cost Paths”. SIGART Newsletter (37), 28–29 (1972)
Dubois, D., Prade, H.: Fundamentals of Fuzzy Sets. Kluwer Acad. Pub., Dordrecht (2000)
Hardy, G.H., Littlewood, J.E., Pólya, G.: Inequalities. Cambridge Univ., Cambridge (1968)
Cloud, M.J., Drachman, B.C.: Inequalities. Springer, Heidelberg (1998)
Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Kluwer Acad., Dordrecht (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gosztolya, G., Kocsor, A. (2004). Aggregation Operators and Hypothesis Space Reductions in Speech Recognition. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2004. Lecture Notes in Computer Science(), vol 3206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30120-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-30120-2_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23049-6
Online ISBN: 978-3-540-30120-2
eBook Packages: Springer Book Archive