PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning the Inter-frame Distance for Discriminative Template-based Keyword Detection
David Grangier and Samy Bengio
In: Eurospeech 2007, 27-31 Aug 2007, Antwerp, Belgium.

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Speech
Theory & Algorithms
ID Code:3300
Deposited By:David Grangier
Deposited On:07 February 2008