Robust heteroscedastic linear discriminant analysis and LCRC posterior features in meeting data recognition

Authors:
Martin Karafiát;Frantiśek Grézl;Petr Schwarz;Lukáš Burget;Jan Černocký
Affiliations:
Speech@FIT, Faculty of Information Technology, Brno University of Technology;Speech@FIT, Faculty of Information Technology, Brno University of Technology;Speech@FIT, Faculty of Information Technology, Brno University of Technology;Speech@FIT, Faculty of Information Technology, Brno University of Technology;Speech@FIT, Faculty of Information Technology, Brno University of Technology
Venue:
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Year:
2006

Citing 2
Cited 2

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Abstract

This paper investigates into feature extraction for meeting recognition. Three robust variants of popular HLDA transform are investigated. Influence of adding posterior features to PLP feature stream is studied. The experimental results are obtained on two data-sets: CTS (continuous telephone speech) and meeting data from NIST RT'05 evaluations. Silence-reduced HLDA and LCRC phoneme-state posterior features are found to be suitable for both recognition tasks.