In this paper, a new text-independent speaker recognition method is proposed. This method uses a modeling of the spectral evolution of the speech signals, which is capable of processing some aspects of the inter-speaker variability : the AR-Vector models. Some inter-speaker measures are presented and their advantages/inconvenients are discussed. A training technique to learn discriminant AR-Vector models is proposed. The evaluation of this method is carried out on the TIMIT database recorded by cooperative speakers without any impostor. A series of text-independent speaker identification experiments are described. There is no specific corpus for the training sentences and the training corpus is different from the test corpus. Two speech qualities are tested (i.e., good quality and phone quality). The experiments with good speech quality give first-rate results (i.e, identification rate of 100% for 420 speakers) without using more than two sentences for each test.
Cite as: Montacié, C., Floch, J.-L.L. (1992) AR-vector models for free-text speaker recognition. Proc. 2nd International Conference on Spoken Language Processing (ICSLP 1992), 611-614, doi: 10.21437/ICSLP.1992-179
@inproceedings{montacie92_icslp,
author={Claude Montacié and Jean-Luc Le Floch},
title={{AR-vector models for free-text speaker recognition}},
year=1992,
booktitle={Proc. 2nd International Conference on Spoken Language Processing (ICSLP 1992)},
pages={611--614},
doi={10.21437/ICSLP.1992-179}
}