Czech HMM-Based Speech Synthesis

Abstract

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.

This work was supported by the Grant Agency of the Czech Republic, project No. GAČR 102/09/0989 and by the Ministry of Education of the Czech Republic, project No. 2C06020. Author would also like to thank Prof. Hideki Kawahara from Wakayama University for his permission to use the STRAIGHT analysis/synthesis method [2]. The access to the MetaCentrum supercomputing facilities provided under the research intent MSM6383917201 is also highly appreciated.