New perspectives on spoken language understanding: Does machine need to fully understand speech?
Tatsuya Kawahara · 2009
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.