This paper presents new results by using our previously proposed on-line Bayesian learning approach for affine transformation parameter estimation in speaker adaptation. The on-line Bayesian learning technique allows updating parameter estimates after each utterance and it can accommodate flexible forms of transformation functions as well as prior probability density functions. We show through experimental results the robustness of heavy tailed priors to mismatch in prior density estimation. We also show that by properly choosing the transformation matrices and depths of hierarchical trees, recognition performance improved significantly.
& Zhao, Y.
(2000). On-Line Bayesian Speaker Adaptation By Using Tree-Structured Transformation and Robust Priors. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2, 977-980.