Effects of word confusion networks on voice search

Abstract

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 confusion-network 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. © 2009 Association for Computational Linguistics.