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Machine Learning for Spoken Dialogue Systems

Abstract : During the last decade, research in the field of Spoken Dialogue Systems (SDS) has experienced increasing growth. However, the design and optimization of SDS is not only about combining speech and language processing systems such as Automatic Speech Recognition (ASR), parsers, Natural Language Generation (NLG), and Text-to-Speech (TTS) synthesis systems. It also requires the development of dialogue strategies taking at least into account the performances of these subsystems (and others), the nature of the task (e.g. form filling, tutoring, robot control, or database search/browsing), and the user's behaviour (e.g. cooperativeness, expertise). Due to the great variability of these factors, reuse of previous hand-crafted designs is also made very difficult. For these reasons, statistical machine learning (ML) methods applied to automatic SDS optimization have been a leading research area for the last few years. In this paper, we provide a short review of the field and of recent advances.
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Submitted on : Tuesday, February 19, 2008 - 7:49:12 PM
Last modification on : Monday, December 14, 2020 - 2:10:02 PM
Long-term archiving on: : Thursday, September 27, 2012 - 5:40:23 PM


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  • HAL Id : hal-00216035, version 1



Olivier Lemon, Olivier Pietquin. Machine Learning for Spoken Dialogue Systems. European Conference on Speech Communication and Technologies (Interspeech'07), Aug 2007, Anvers, Belgium. pp.2685-2688. ⟨hal-00216035⟩



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