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Generalising power of a learned Hierarchical Hidden Markov Model

Lament, O. and Mann, G. (2005) Generalising power of a learned Hierarchical Hidden Markov Model. In: Eighth IASTED International Conference on Intelligent Systems and Control, ISC 2005, 31 October - 2 November, Cambridge, MA; USA pp. 291-296.


In this work we explore how applying graph-modifying algorithms to a Hierarchical Hidden Markov Model can simplify the specification of input-output mapping rules in a spoken language dialogue system. A set of state-chunking learning algorithms capable of inducing a stochastic context-free grammar from a small amount of question-and-answer training data have been created for use in the Speech Librarian, our test implementation. We quantitatively estimate the power of the system to induce a broad but accurate coverage of linguistic queries from a relatively small set of question-and-answer pairs, using subjective judgements of semantic relevance weighted by probability of occurrence.

Item Type: Conference Paper
Murdoch Affiliation(s): School of Information Technology
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