Feature space variational Bayesian linear regression and its combination with model space VBLR
Seongjun Hahm, Atsunori Ogawa, Marc Delcroix, Masakiyo Fujimoto, Takaaki Hori, Atsushi Nakamura · 2013
In this paper, we propose a tuning-free Bayesian linear regression approach for speaker adaptation. We first formulate feature space variational Bayesian linear regression (fVBLR). Using a lower bound as the objective function, we can optimize a binary tree structure and control parameters for prior density scaling. We experimentally verified the proposed fVBLR could achieve performance comparable to that of the conventional fine-tuned fSMAPLR and SMAPLR. For further performance improvement regardless of the amount of adaptation data, we combine fVBLR with model space VBLR (fVBLR+VBLR). Therefore, feature space normalization and model space adaptation are consistently performed based on a variational Bayesian approach without any tuning parameters. In the experiment, the proposed fVBLR+VBLR showed performance improvement compared with both fVBLR and VBLR.