The i-vector framework is one of the most popular methods in speaker verification, and estimating a total variability space (TVS) is a key part in the i-vector framework. Current estimation methods pay less attention on the discrimination of TVS, but the discrimination is so important that it will influence the improvement of performance. So we focus on the discrimination of TVS to achieve a better performance. In this paper, a discriminative estimating method of TVS based on probabilistic partial least squares (PPLS) is proposed. In this method, the discrimination is improved by using the priori information (labels) of speaker, so both the correlation of intra-class and the discrimination of interclass are fully utilized. Meanwhile, it also introduces a probabilistic view of the partial least squares (PLS) method to overcome the disadvantage of high computational complexity and the inability of channel compensation. And also this proposed method can achieve a better performance than the traditional TVS estimation method as well as the PLS-based method.
Cite as: Chen, C., Han, J., Pan, Y. (2017) Speaker Verification via Estimating Total Variability Space Using Probabilistic Partial Least Squares. Proc. Interspeech 2017, 1537-1541, doi: 10.21437/Interspeech.2017-633
@inproceedings{chen17f_interspeech,
author={Chen Chen and Jiqing Han and Yilin Pan},
title={{Speaker Verification via Estimating Total Variability Space Using Probabilistic Partial Least Squares}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={1537--1541},
doi={10.21437/Interspeech.2017-633}
}