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
expansion by simulation. In this book, we provide an overview of
the current state of the field and of recent advances, with a
specific focus on adaptivity.
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