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| Content Provider | ACM Digital Library |
|---|---|
| Author | Vogt, Robbie Wallace, Roy Sridharan, Sridha Baker, Brendan |
| Abstract | Spoken term detection (STD) popularly involves performing word or sub-word level speech recognition and indexing the result. This work challenges the assumption that improved speech recognition accuracy implies better indexing for STD. Using an index derived from phone lattices, this paper examines the effect of language model selection on the relationship between phone recognition accuracy and STD accuracy. Results suggest that language models usually improve phone recognition accuracy but their inclusion does not always translate to improved STD accuracy. The findings suggest that using phone recognition accuracy to measure the quality of an STD index can be problematic, and highlight the need for an alternative that is more closely aligned with the goals of the specific detection task. |
| Starting Page | 31 |
| Ending Page | 36 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781605587622 |
| DOI | 10.1145/1631127.1631132 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-10-23 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Phoneme recognition Phoneme lattice Language modelling Spoken term detection |
| Content Type | Text |
| Resource Type | Article |
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