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
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
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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