Voice search of structured media data

This paper addresses the problem of using unstructured queries to search a structured database in voice search applications. By incorporating structural information in music metadata, the end-to-end search error has been reduced by 15% on text queries and up to 11% on spoken queries. Based on that,...

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Bibliographic Details
Main Authors: Young-In Song, Ye-Yi Wang, Yun-Cheng Ju, Seltzer, M., Tashev, I., Acero, A.
Format: Conference Proceeding
Language:English
Subjects:
Arm
Computer errors
Costs
Error analysis
Hidden Markov models
HMMs
language model based information retrieval
Motion pictures
Music information retrieval
music metadata
Natural languages
phonetic confusability
Speech recognition
spoken language understanding
voice search
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Summary:This paper addresses the problem of using unstructured queries to search a structured database in voice search applications. By incorporating structural information in music metadata, the end-to-end search error has been reduced by 15% on text queries and up to 11% on spoken queries. Based on that, an HMM sequential rescoring model has reduced the error rate by 28% on text queries and up to 23% on spoken queries compared to the baseline system. Furthermore, a phonetic similarity model has been introduced to compensate speech recognition errors, which has improved the end-to-end search accuracy consistently across different levels of speech recognition accuracy.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2009.4960490