期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2009
卷号：2009
出版社：ACL Anthology
摘要：Mobile voice-enabled search is emerging
as one of the most popular applications
abetted by the exponential growth in the
number of mobile devices. The automatic
speech recognition (ASR) output of the
voice query is parsed into several fields.
Search is then performed on a text corpus
or a database. In order to improve the robustness
of the query parser to noise in the
ASR output, in this paper, we investigate
two different methods to query parsing.
Both methods exploit multiple hypotheses
from ASR, in the form of word confusion
networks, in order to achieve tighter coupling
between ASR and query parsing and
improved accuracy of the query parser. We
also investigate the results of this improvement
on search accuracy. Word confusionnetwork
based query parsing outperforms
ASR 1-best based query-parsing by 2.7%
absolute and the search performance improves
by 1.8% absolute on one of our data
sets.
