Excitation Modeling Method Based on Inverse Filtering for HMM-Based Speech Synthesis

  • M. Kiran ReddyEmail author
  • K. Sreenivasa Rao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)


In this paper, we propose a novel excitation modeling approach for HMM-based speech synthesis system (HTS). Here, the excitation signal obtained via inverse filtering is parameterized into excitation features, which are modeled using HMMs. During synthesis, the excitation signal is reconstructed by modifying the natural residual segments in accordance with the target source features generated from HMMs. The proposed approach is incorporated into the HTS. Subjective evaluation results indicate that the proposed method enhances the quality of synthesis and is better than the traditional pulse and STRAIGHT-based excitation models.


  1. 1.
    Cabral, J.P.: Uniform concatenative excitation model for synthesizing speech without voiced/unvoiced classification. In: Proceedings of Interspeech, pp. 1082–1086 (2013)Google Scholar
  2. 2.
    Drugman, T., Dutoit, T.: The deterministic plus stochastic model of the residual signal and its applications. IEEE Trans. Audio Speech Lang. process. 20(3), 968–981 (2012)Google Scholar
  3. 3.
    Drugman, T., Raitio, T.: Excitation modeling for HMM-based speech synthesis: breaking down the impact of periodic and aperiodic components. In: Proceedings of International Conference on Audio, Speech and Signal Processing (ICASSP), pp. 260–264 (2014)Google Scholar
  4. 4.
    HMM-based speech synthesis system (HTS).
  5. 5.
    Kawahara, H., Masuda-Katsuse, I., de Cheveigne, A.: Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous frequency-based F0 extraction: possible role of a repetitive structure in sounds. Speech Commun. 27, 187–207 (1999)CrossRefGoogle Scholar
  6. 6.
    Kiran Reddy M., Sreenivasa Rao, K.: Robust pitch extraction Method for the HMM-Based speech synthesis system: IEEE Signal Process. Lett. 24(8), 1133–1137 (2017)Google Scholar
  7. 7.
    Kominek, J., Black, A.: The CMU arctic speech databases. In: Proceedings of ISCA Speech Synthesis Workshop, pp. 223–224 (2004)Google Scholar
  8. 8.
    Maia, R., Toda, T., Zen, H., Nankaku, Y., Tokuda, K.: An excitation model for HMM-based speech synthesis based on residual modeling. In: Proceedings of ISCA Speech Synthesis Workshop, pp. 131–136 (2007)Google Scholar
  9. 9.
    Narendra, N.P., Sreenivasa Rao, K.: A deterministic plus noise model of excitation signal using principal component analysis for parametric speech synthesis. In: Proceedings of International Conference on Audio, Speech and Signal Processing (ICASSP), pp. 5635–5639 (2016)Google Scholar
  10. 10.
    Narendra, N.P., Kiran Reddy M., Sreenivasa Rao K.: Excitation modeling for HMM-based speech synthesis based on principal component analysis. In: proceedings of IEEE National Conference on Communication (NCC), pp. 1–6 (2016)Google Scholar
  11. 11.
    Raitio, T., Suni, A., Yamagishi, J., Pulakka, H., Nurminen, J., Vainio, M., Alku, P.: HMM-based speech synthesis utilizing glottal inverse filtering. IEEE Trans. Audio, Speech, Lang. Process. 19(1), 153–165 (2011)Google Scholar
  12. 12.
    Tokuda, K., et al.: Speech synthesis based on hidden Markov models. Proc. IEEE 101(5), 1234–1252 (2013)CrossRefGoogle Scholar
  13. 13.
    Wen Z., Tao J., Hain H.-U.: Pitch-scaled spectrum based excitation model for HMM-based speech synthesis. In: Proceedings of IEEE international conference on Signal Processing (ICSP), pp. 609–612 (2012)Google Scholar
  14. 14.
    Yoshimura, T., Tokuda, K., Masuko, T., Kobayashi, T., Kitamura, T.: Mixed-excitation for HMM-based speech synthesis. In: Proceedings of the Eurospeech, pp. 2259–2262 (2001)Google Scholar
  15. 15.
    Zen, H., Toda, T., Nakamura, M., Tokuda, K.: Details of Nitech HMM-based speech synthesis system for the Blizzard Challenge 2005. IEICE Trans. Inform. Syst. E90-D, 325–333 (2007)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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