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Comparing isolately spoken keywords with spontaneously spoken queries for Japanese spoken document retrieval

This paper describes a Japanese spoken document retrieval system that uses voice input queries. We prepare two types of spoken queries: isolately spoken keywords and spontaneously spoken queries. To solve a mis-recognition problem of spoken queries, N-best hypotheses of transcripts of queries are used, and keyword candidates are selected from them by mutual information between recognized words. Using both an effective keyword selecting algo-rithm and spontaneously spoken queries instead of isolately spoken keywords, a retrieval performance in F-measure improves to 73.4 from 72.9, and using N-best hypotheses by recognizing queries, the performance also improves to 73.4 from 68.7.