Most of the current text-independent speaker veri,cation techniques are based on modelling the global probability distribution function of speakers in the acoustic vector space. We present an alternative approach based on class-dependent veri,cation systems using automatically determined segmental units, obtained with temporal decomposition and labelled through unsupervised clustering. The core of the system is a set of multi-layer perceptrons (MLP) trained to discriminate between client and an independent set of world speakers. Each MLP is dedicated to work with data segments that are previously selected as belonging to a particular class. Issues and potential advantages of the segmental approach are presented. Performances of global and segmental approaches are tested on the NIST'98 database (250 female and 250 male speakers), showing promising results for the proposed new segmental approach. Comparison with a state of the art system, based on Gaussian Mixture Modelling is also included.
Cite as: Petrovska-Delacretaz, D., Cernocky, J., Hennebert, J., Chollet, G. (1998) Text-independent speaker verification using automatically labelled acoustic segments. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0536, doi: 10.21437/ICSLP.1998-249
@inproceedings{petrovskadelacretaz98_icslp,
author={Dijana Petrovska-Delacretaz and Jan Cernocky and Jean Hennebert and Gérard Chollet},
title={{Text-independent speaker verification using automatically labelled acoustic segments}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0536},
doi={10.21437/ICSLP.1998-249}
}