Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Structural Framework for Combining Speaker Recognition Methods

Claude Montacié, Marie-José Caraty

LIP6 - Université Pierre et Marie Curie, Paris, France

The paper describes a structural framework for the design of a speaker recognition system based on multiple models. This combination is not only at the recognition level, but also at a joint training of the models. This unified training of the models uses a common structure : a decomposition tree of the set of data of normalization speakers. For the experiments, the Gaussian Mixture Model and the Auto-Regressive Vectorial Model are the two models we have selected to test the structural framework of the speaker verification scoring combination. This approach has been tested on a subset of the 30"-NIST’97 Speaker Recognition Evaluation corpus. The list of the files of this subset (i.e., normalization, training and test) can be found at http://www-apa.lip6.fr/PAROLE/ICSLP2000/.


Full Paper

Bibliographic reference.  Montacié, Claude / Caraty, Marie-José (2000): "Structural framework for combining speaker recognition methods", In ICSLP-2000, vol.2, 479-482.