He, Yulan and Young, S.
A data-driven spoken language understanding system.
In: 2003 IEEE Workshop on Automatic Speech Recognition and Understanding: ASRU '03, 30 Nov - Dec 2003, St. Thomas, Virgin Islands, pp. 583–588.
Full text available as:
Due to copyright restrictions, this file is not available for public download
The paper presents a purely data-driven spoken language understanding (SLU) system. It consists of three major components, a speech recognizer, a semantic parser, and a dialog act decoder. A novel feature of the system is that the understanding components are trained directly from data without using explicit semantic grammar rules or fully annotated corpus data. Despite this, the system is nevertheless able to capture hierarchical structure in user utterances and handle long range dependencies. Experiments have been conducted on the ATIS corpus and 16.1% and 12.6% utterance understanding error rates were obtained for spoken input using the ATIS-3 1993 and 1994 test sets. These results show that our system is comparable to existing SLU systems which rely on either handcrafted semantic grammar rules or statistical models trained on fully-annotated training corpora, but it has greatly reduced build cost.
||INSPEC Accession Number: 8072860
||data-driven spoken language understanding system; dialog act decoder; fully-annotated training corpora; handcrafted semantic grammar rules; hierarchical structure; semantic parser; speech recognition; spoken dialogue systems; statistical models; utterance understanding error rates
||Knowledge Media Institute
|Interdisciplinary Research Centre:
||Centre for Research in Computing (CRC)
||29 Mar 2011 10:20
||26 Oct 2012 23:26
Actions (login may be required)