Telephone speech recognition via the combination of knowledge sources in a segmental speech model

Tóth, László and Kocsor, András and Gosztolya, Gábor: Telephone speech recognition via the combination of knowledge sources in a segmental speech model. In: Acta cybernetica, (16) 4. pp. 643-657. (2004)

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The currently dominant speech recognition methodology, Hidden Markov Modeling, treats speech as a stochastic random process with very simple mathematical properties. The simplistic assumptions of the model, and especially that of the independence of the observation vectors have been criticized by many in the literature, and alternative solutions have been proposed. One such alternative is segmental modeling, and the OASIS recognizer we have been working on in the recent years belongs to this category. In this paper we go one step further and suggest that we should consider speech recognition as a knowledge source combination problem. We offer a generalized algorithmic framework for this approach and show that both hidden Markov and segmental modeling are a special case of this decoding scheme. In the second part of the paper we describe the current components of the OASIS system and evaluate its performance on a very difficult recognition task, the phonetically balanced sentences of the MTBA Hungarian Telephone Speech Database. Our results show that OASIS outperforms a traditional HMM system in phoneme classification and achieves practically the same recognition scores at the sentence level.

Item Type: Article
Journal or Publication Title: Acta cybernetica
Date: 2004
Volume: 16
Number: 4
ISSN: 0324-721X
Page Range: pp. 643-657
Language: angol
Event Title: Conference on Hungarian Computational Linguistics, 1., 2003, Szeged
Related URLs:
Uncontrolled Keywords: Természettudomány, Informatika, Nyelvtudomány
Additional Information: Bibliogr.: p. 655-657.; Abstract
Date Deposited: 2016. Oct. 15. 12:25
Last Modified: 2021. Mar. 24. 14:43

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