Speaker Adaptation Using Nonlinear Regression Techniques for HMM-Based Speech Synthesis

The maximum likelihood linear regression (MLLR) technique is a well-known approach to parameter adaptation in hidden Markov model (HMM)-based systems. In this paper, we propose the maximum penalized likelihood kernel regression (MPLKR) approach as a novel adaptation technique for HMM-based speech synthesis. The proposed algorithm performs a nonlinear regression between the mean vector of the base model and the corresponding mean vector of adaptive data by means of a kernel method. In the experiments, we used various types of parametric kernels for the proposed algorithm and compared their performances with the conventional method. From experimental results, it has been found that the proposed algorithm outperforms the conventional method in terms of the objective measure as well as the subjective listening quality.