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 ro- bustness 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 cou- pling between ASR and query parsing and improvedaccuracyofthequeryparser. We also investigate theresults of this improve- ment on search accuracy. Word confusion- network based query parsing outperforms ASR 1-best based q...

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