In this paper, we propose a Bayesian speaker adaptation technique based on the probabilistic principal component analysis (PPCA). The PPCA is employed to obtain the canonical speaker models which provide the a priori knowledge of the training speakers. The proposed approach is conveniently incorporated into the Bayesian adaptation framework where the parameters are adapted to the new speakers speech according to the maximum a posteriori (MAP) criterion. Through a number of continuous digit recognition experiments, we can find the effectiveness of the PPCA-based approach compared to the other adaptation approaches with a small amount of adaptation data.
Cite as: Kim, D.K., Kim, N.S. (2000) Bayesian speaker adaptation based on probabilistic principal component analysis. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 3, 734-737, doi: 10.21437/ICSLP.2000-640
@inproceedings{kim00d_icslp,
author={Dong Kook Kim and Nam Soo Kim},
title={{Bayesian speaker adaptation based on probabilistic principal component analysis}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 3, 734-737},
doi={10.21437/ICSLP.2000-640}
}