Telephone Speech Recognition via the Combination of Knowledge Sources in a Segmental Speech Model1

 László Tóth2, András Kocsor, and Gábor Gosztolya

  Acta Cybernetica 16 (2004) 643-657.

Abstract:

 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.

Footnotes

 Model 1 Presented at the 1st Conference on Hungarian Computational Linguistics, December 10-11, 2003, Szeged.

 Tóth 2 Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, H-6720 Szeged, Aradi vértanúk tere 1., Hungary, e-mail: tothl@inf.u-szeged.hu, kocsor@inf.u-szeged.hu, ggabor@inf.u-szeged.hu