Incorporating a mixed excitation model and postfilter into HMM-based text-to-speech synthesis
Systems and Computers in Japan
IEICE - Transactions on Information and Systems
Generating F0 Contours by Statistical Manipulation of Natural F0 Shapes
IEICE - Transactions on Information and Systems
Unit selection in a concatenative speech synthesis system using a large speech database
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Selecting non-uniform units from a very large corpus for concatenative speech synthesizer
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
The Nitech-NAIST HMM-Based Speech Synthesis System for the Blizzard Challenge 2006
IEICE - Transactions on Information and Systems
Review: Statistical parametric speech synthesis
Speech Communication
Robust speaker-adaptive HMM-based text-to-speech synthesis
IEEE Transactions on Audio, Speech, and Language Processing
Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
IEEE Transactions on Audio, Speech, and Language Processing
Statistical text-to-speech synthesis based on segment-wise representation with a norm constraint
IEEE Transactions on Audio, Speech, and Language Processing
High quality emotional HMM-Based synthesis in spanish
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
INPRO_iSS: a component for just-in-time incremental speech synthesis
ACL '12 Proceedings of the ACL 2012 System Demonstrations
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Complex cepstrum for statistical parametric speech synthesis
Speech Communication
Synthesis and perception of breathy, normal, and Lombard speech in the presence of noise
Computer Speech and Language
| Hi-index | 0.00 |
This paper describes a novel parameter generation algorithm for an HMM-based speech synthesis technique. The conventional algorithm generates a parameter trajectory of static features that maximizes the likelihood of a given HMM for the parameter sequence consisting of the static and dynamic features under an explicit constraint between those two features. The generated trajectory is often excessively smoothed due to the statistical processing. Using the over-smoothed speech parameters usually causes muffled sounds. In order to alleviate the over-smoothing effect, we propose a generation algorithm considering not only the HMM likelihood maximized in the conventional algorithm but also a likelihood for a global variance (GV) of the generated trajectory. The latter likelihood works as a penalty for the over-smoothing, i.e., a reduction of the GV of the generated trajectory. The result of a perceptual evaluation demonstrates that the proposed algorithm causes considerably large improvements in the naturalness of synthetic speech.