2009年
New Perspectives on Spoken Language Understanding: Does Machine Need to Fully Understand Speech?
2009 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION & UNDERSTANDING (ASRU 2009)
- 開始ページ
- 46
- 終了ページ
- 50
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- 出版者・発行元
- IEEE
Spoken Language Understanding (SLU) has been traditionally formulated to extract meanings or concepts of user utterances in the context of human-machine dialogue. With the broadened coverage of spoken language processing, the tasks and methodologies of SLU have been changed accordingly. The back-end of spoken dialogue systems now consist of not only relational databases (RDB) but also general documents, incorporating information retrieval (IR) and question-answering (QA) techniques. This paradigm shift and the author's approaches are reviewed. SLU is also being designed to cover human-human dialogues and multi-party conversations. Major approaches to "understand" human-human speech communication and a new approach based on the lister's reactions are reviewed. As a whole, these trends are apparently not oriented for full understanding of spoken language, but for robust extraction of clue information.
- リンク情報
- ID情報
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- Web of Science ID : WOS:000291368500011