Decision tree-based context clustering based on cross validation and hierarchical priors

The standard, ad-hoc stopping criteria used in decision tree-based context clustering are known to be sub-optimal and require parameters to be tuned. This paper proposes a new approach for decision tree-based context clustering based on cross validation and hierarchical priors. Combination of cross validation and hierarchical priors within decision tree-based context clustering offers better model selection and more robust parameter estimation than conventional approaches, with no tuning parameters. Experimental results on HMM-based speech synthesis show that the proposed approach achieved significant improvements in naturalness of synthesized speech over the conventional approaches.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Heiga Zen, Mark J. F. Gales
• Keywords: Conventional Approaches, Hierarchical Priors, ICASSP 2011, Signal Processing, Tree-based Context