Abstract

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. 1. EIGENVOICES WITH MLLR MODELS Using the eigenvoices approach in combination with MLLR is not a new idea. In this section, we will briefly introduce the notation and fundamental equations used in the next sections. 1.1. Gaussianity of MLLR rows Speaker dependent models are needed to build the eigenspace. However, for large vocabulary applications, building these models is difficult because of data sparsity and memory requirements. In practice, most systems use MLLR-adapted models [1]. MLLR transforms model means by a matrix: We are concerned with the adaptation of mean vectors, with diagonal covariance matrices. The expected loglikelihood after E-step of the Baum-Welch algorithm is 4

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