A novel irregular voice model for HMM-based speech synthesis

State-of-the-art text-to-speech (TTS) synthesis is often based on statistical parametric methods. Particular attention is paid to hidden Markov model (HMM) based text-to-speech synthesis. HMM-TTS is optimized for ideal voices and may not produce high quality synthesized speech with voices having frequent non-ideal phonation. Such a voice quality is irregular phonation (also called as glottalization), which occurs frequently among healthy speakers. There are existing methods for transforming regular (also called as modal) to irregular voice, but only initial experiments have been conducted for statistical parametric speech synthesis with a glottalization model. In this paper we extend our previous residual codebook based excitation model with irregular voice modeling. The proposed model applies three heuristics, which were proven to be useful: 1) pitch halving, 2) pitchsynchronous residual modulation with periods multiplied by random scaling factors and 3) spectral distortion. In a perception test the extended HMM-TTS produced speech that is more similar to the original speaker than the baseline system. An acoustic experiment found the output of the model to be similar to original irregular speech in terms of several parameters. Applications of the model may include expressive statistical parametric speech synthesis and the creation of personalized voices.