Weakly Supervised PLDA Training
Abstract
PLDA is a popular normalization approach for the i-vector model, and it has delivered state-of-the-art performance in speaker verification. However, PLDA training requires a large amount of labelled development data, which is highly expensive in most cases. We present a cheap PLDA training approach, which assumes that speakers in the same session can be easily separated, and speakers in different sessions are simply different. This results in `weak labels' which are not fully accurate but cheap, leading to a weak PLDA training. Our experimental results on real-life large-scale telephony customer service achieves demonstrated that the weak training can offer good performance when human-labelled data are limited. More interestingly, the weak training can be employed as a discriminative adaptation approach, which is more efficient than the prevailing unsupervised method when human-labelled data are insufficient.
Cite
@article{arxiv.1609.08441,
title = {Weakly Supervised PLDA Training},
author = {Lantian Li and Yixiang Chen and Dong Wang and Chenghui Zhao},
journal= {arXiv preprint arXiv:1609.08441},
year = {2017}
}