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Technical Program

Paper Detail

Paper:	SLP-P1.2
Session:	Feature Extraction and Modeling
Time:	Tuesday, May 16, 10:30 - 12:30
Presentation:	Poster
Topic:	Speech and Spoken Language Processing: Clustering and novel modeling algorithms
Title:	Flexible Feature Spaces based on Generalized Heteroscedastic Linear Discriminant Analysis
Authors:	Alessandro Duminuco, Institut Eurecom, France; Chaojun Liu, David Kryze, Luca Rigazio, Panasonic Digital Networking Laboratory, United States
Abstract:	This paper presents a generalized feature projection scheme which allows each feature dimension to be classified in a set of 1 to M classes, where M is the total number of classes. Our method is an extension of the classical full-space null-space approach where each dimension can only be classified in either M classes or 1 class. We believe that this more general formulation allows for a better trade-off of number of parameters versus model complexity, which in turn should provide better classification. We first tested GLDA on TIMIT and obtained an improvement up to 1% in phone classification rate over the best HLDA classifier. Preliminary results on Wall Street Journal 20K also show an improvement over the best HLDA system of about 0.2% absolute.