Details of the Nitech HMM-Based Speech Synthesis System for the Blizzard Challenge 2005
IEICE - Transactions on Information and Systems
Prosodic Phrases and Semantic Accents in Speech Corpus for Czech TTS Synthesis
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Review: Statistical parametric speech synthesis
Speech Communication
Recording and annotation of speech corpus for Czech unit selection speech synthesis
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Czech HMM-based speech synthesis: experiments with model adaptation
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Comparison of complementary spectral features of emotional speech for german, czech, and slovak
COST'11 Proceedings of the 2011 international conference on Cognitive Behavioural Systems
| Hi-index | 0.00 |
In this paper, first experiments on statistical parametric HMM-based speech synthesis for the Czech language are described. In this synthesis method, trajectories of speech parameters are generated from the trained hidden Markov models. A final speech waveform is synthesized from those speech parameters. In our experiments, spectral properties were represented by mel cepstrum coefficients. For the waveform synthesis, the corresponding MLSA filter excited by pulses or noise was utilized. Beside that basic setup, a high-quality analysis/synthesis system STRAIGHT was employed for more sophisticated speech representation. For a more robust model parameter estimation, HMMs are clustered by using decision tree-based context clustering algorithm. For this purpose, phonetic and prosodic contextual factors proposed for the Czech language are taken into account. The created clustering trees are also employed for synthesis of speech units unseen within the training stage. The evaluation by subjective listening tests showed that speech produced by the combination of HMM-based TTS system and STRAIGHT is of comparable quality as speech synthesised by the unit selection TTS system trained from the same speech data.