Abstract-This paper proposes a statistical parametric speech synthesis technique based on Gaussian process regression (GPR). The GPR model is designed for directly predicting frame-level acoustic features from corresponding information on frame context that is obtained from linguistic information. The frame context includes the relative position of the current frame within the phone and articulatory information and is used as the explanatory variable in GPR. Here, we introduce cluster-based sparse Gaussian processes (GPs), i.e., local GPs and partially independent conditional (PIC) approximation, to reduce the computational cost. The experimental results for both isolated phone synthesis and full-sentence continuous speech synthesis revealed that the proposed GPR-based technique without dynamic features slightly outperformed the conventional hidden Markov model (HMM)-based speech synthesis using minimum generation error training with dynamic features. Index Terms-Gaussian process regression, nonparametric Bayesian model, partially independent conditional (PIC) approximation, sparse Gaussian processes, statistical speech synthesis.
Speech synthesis based on Gaussian process regression (GPR) REF is another new technique that has recently been introduced to alleviate basic limitations of HMMbased speech synthesis.
Statistical Parametric Speech Synthesis Based on Gaussian Process Regression
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing
Computer Science