CUED Publications database

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

Zen, H and Gales, MJF (2011) Decision tree-based context clustering based on cross validation and hierarchical priors. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp. 4560-4563. ISSN 1520-6149

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Abstract

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. © 2011 IEEE.

Item Type: Article
Uncontrolled Keywords: cross validation decision tree-based context clustering hierarchical priors HMM-based speech synthesis
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:57
Last Modified: 08 Dec 2014 02:36
DOI: 10.1109/ICASSP.2011.5947369