[PDF.61ic] Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces

Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces

[PDF.fg15] Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces

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 | #4703127 in Books |  Springer |  2012-10-21 | Original language:English | PDF # 1 |  9.30 x.60 x6.10l,1.10 | File type: PDF | 178 pages

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||From the Back Cover||The EC FP7 project “Computational Learning in Adaptive Systems for Spoken Conversation” (CLASSiC) was a European initiative working on a fully data-driven architecture for the development of conversational interfaces, as well as

Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present “end-to-end” in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expan...

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