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Robust Multi-Keyword Spotting of Telephone Speech Using Stochastic Matching
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wu, Chung-Hsien Chen, Yeou-Jiunn Hung, Yu-Chun |
| Copyright Year | 2009 |
| Abstract | In telephone speech recognition, the acoustic mismatch between the training and the test environment often causes severe degradation due to the channel distortion and ambient noise. In this paper, a two-level codebook-based stochastic matching (CBSM) is proposed to deal with the acoustic mismatch. For multi-keyword detection, we define a keyword relation table and a weighting function for reasonable keyword combinations. In the multi-keyword spotting system, 94 right context-dependent INITIAL’s, 37 context-independent FINAL’s and 1 silence model are adopted. In order to evaluate the multi-keyword spotting system, 1275 faculty names and department names are selected as the keywords. Using a testing set of 2400 conversional speech utterances from 8 speakers, the proposed two-level CBSM can reduce the recognition error rate from 36.52% to 13.4%. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.researchgate.net/profile/Yeou-Jiunn_Chen/publication/228407327_Robust_Multi-Keyword_Spotting_of_Telephone_Speech_Using_Stochastic_Matching/links/0912f50eea68e5c621000000.pdf |
| Alternate Webpage(s) | https://www.researchgate.net/profile/Yeou-Jiunn_Chen/publication/228407327_Robust_Multi-Keyword_Spotting_of_Telephone_Speech_Using_Stochastic_Matching/links/0912f50eea68e5c621000000.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
