Context-dependent deterministic plus stochastic model

This article proposes a method to improve the performance of deterministic plus stochastic model (DSM-) based feature extraction by integrating the contextual information. One precious advantage of speech synthesis over speech recognition is that in both training and testing phases of synthesis, contextual information is available. However, similar to recognition, this invaluable knowledge has been forgotten during acoustic feature extraction of speech synthesis. DSM expresses the residual of Mel-cepstral analysis through a summation of two components, namely deterministic and stochastic. This study proposes to model the deterministic component through a novel context-dependent principal component analysis (CD-PCA), and the stochastic component through the conventional high-pass filtered noise. Furthermore, due to the high dependency of the proposed feature extraction on state boundaries, the feature analysis and HMM-based modeling are performed in an iterative manner. Subjective evaluations conducted on a Persian speech database confirm the effectiveness of the proposed synthesis system.