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Erschienen in:
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2012 | OriginalPaper | Buchkapitel

3. Data-Driven Methods for Spoken Language Understanding

verfasst von : James Henderson, Filip Jurčíček

Erschienen in: Data-Driven Methods for Adaptive Spoken Dialogue Systems

Verlag: Springer New York

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Abstract

Spoken dialogue systems need to be able to interpret the spoken input from theuser. This is done by mapping the user’s spoken utterance to a representation ofthe meaning of that utterance, and then passing this representation to thedialogue manager. This process begins with the application of automatic speechrecognition (ASR) technology, which maps the speech to hypotheses about thesequence of words in the utterance. It is then the job of spoken languageunderstanding (SLU) to map the word recognition hypotheses to hypothesisedmeanings. The representation of this meaning is called the semantics of theutterance.

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Vorheriges Kapitel Developing Dialogue Managers from Limited Amounts of Data
Nächstes Kapitel User Simulation in the Development of Statistical Spoken Dialogue Systems
Fußnoten
1
Similar experiments are reported in [6], where generative models are reranked with discriminative models, and in [7], where they focus on the advantages of discriminative reranking for small datasets.
 
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Metadaten
Titel
Data-Driven Methods for Spoken Language Understanding
verfasst von
James Henderson
Filip Jurčíček
Copyright-Jahr
2012
Verlag
Springer New York
Buch
Data-Driven Methods for Adaptive Spoken Dialogue Systems

Print ISBN: 978-1-4614-4802-0

Electronic ISBN: 978-1-4614-4803-7

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-1-4614-4803-7_3

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