Piecewise linear constraints for model space adaptation

Setting linear constraints on HMM model space appears to be very effective for speaker adaptation. In doing so, we assume that model parameters are jointly Gaussian. While this approach has proven reasonably successful, we question it accuracy in the case of very high dimensionality parameter spaces. To address this problem, we employ a hierarchical piecewise linear model. Gross speaker variations are modeled with a linear eigenspace, subsuming the joint Gaussian model, and finer residues are modeled using another eigenspace chosen depending on the location of the first values. We perform experiments on Wall Street Journal (WSJ) dictation task, and we observe a cumulative 1.3% WER improvement (11 % relative) when using self-adaptation.