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 1520-6149Full text not available from this repository.
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.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:28|
|Last Modified:||23 Apr 2017 02:52|