Czech HMM-Based Speech Synthesis

  • Zdeněk Hanzlíček
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6231)


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.


HMM-based speech synthesis TTS Czech language 


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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zdeněk Hanzlíček
    • 1
  1. 1.Department of CyberneticsUniversity of West BohemiaPilsenCzech Republic

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