8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Learning the Inter-Frame Distance for Discriminative Template-Based Keyword Detection

David Grangier (1), Samy Bengio (2)

(1) IDIAP Research Institute, Switzerland
(2) Google Inc., USA

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.

Full Paper

Bibliographic reference.  Grangier, David / Bengio, Samy (2007): "Learning the inter-frame distance for discriminative template-based keyword detection", In INTERSPEECH-2007, 902-905.