Uncertainty in Artificial Intelligence
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Lexical Access for Speech Understanding using Minimum Message Length Encoding
Ian Thomas, Ingrid Zukerman, Jonathan Oliver, David Albrecht, Bhavani Raskutti
Abstract:
The Lexical Access Problem consists of determining the intended sequence of words corresponding to an input sequence of phonemes (basic speech sounds) that come from a low-level phoneme recognizer. In this paper we present an information-theoretic approach based on the Minimum Message Length Criterion for solving the Lexical Access Problem. We model sentences using phoneme realizations seen in training, and word and part-of-speech information obtained from text corpora. We show results on multiple-speaker, continuous, read speech and discuss a heuristic using equivalence classes of similar sounding words which speeds up the recognition process without significant deterioration in recognition accuracy.
Keywords: Lexical access, speech recognition, inductive inference, minimum message length crit
Pages: 464-471
PS Link: http://www.cs.monash.edu.au/~iant/work/uai97.ps
PDF Link: /papers/97/p464-thomas.pdf
BibTex:
@INPROCEEDINGS{Thomas97,
AUTHOR = "Ian Thomas and Ingrid Zukerman and Jonathan Oliver and David Albrecht and Bhavani Raskutti",
TITLE = "Lexical Access for Speech Understanding using Minimum Message Length Encoding",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "464--471"
}


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