With the proliferation of handheld devices, information access on mobile devices is a topic of growing relevance. This paper presents a system that allows the user to search for information on mobile devices using spoken natural-language queries. We explore several issues related to the creation of this system, which combines state-of-the-art speech-recognition and information-retrieval technologies. This is the first work that we are aware of which evaluates spoken query based information retrieval on a commonly available and well researched text database, the Chinese news corpus used in National Institute of Standards and Technology (NIST)\u2019s TREC-5 and TREC-6 benchmarks. To compare spoken-query retrieval performance for different relevant scenarios and recognition accuracies, the benchmark queries\u2014read verbatim by 20 speakers\u2014were recorded simultaneously through three channels: headset microphone, PDA microphone, and cellular phone. Our results show that for mobile devices with high-quality microphones, spoken-query retrieval based on existing technologies yields retrieval precisions that come close to that for perfect text input (mean average precision 0.459 and 0.489, respectively, on TREC-6).<\/p>\n","protected":false},"excerpt":{"rendered":"
With the proliferation of handheld devices, information access on mobile devices is a topic of growing relevance. This paper presents a system that allows the user to search for information on mobile devices using spoken natural-language queries. We explore several issues related to the creation of this system, which combines state-of-the-art speech-recognition and information-retrieval technologies. 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