Learning the Inter-frame Distance for Discriminative Template-based Keyword Detection
| Type of publication: | Idiap-RR |
| Citation: | grangier:2007:rr_07-15 |
| Number: | Idiap-RR-15-2007 |
| Year: | 2007 |
| Institution: | IDIAP |
| Abstract: | This paper proposes a discriminative approach to template-based keyword detection. We introduce a method to learn the distance used to compare acoustic frames, a crucial element for template matching approaches. The proposed algorithm estimates the distance from data, with the objective to produce a detector maximizing the Area Under the receiver operating Curve (AUC,',','), i.e. the standard evaluation measure for the keyword detection problem. The experiments performed over a large corpus, SpeechDatII, suggest that our model is effective compared to an HMM system, e.g. the proposed approach reaches 93.8\% of averaged AUC compared to 87.9\% for the HMM. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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