International Journal of Speech Technology

, Volume 22, Issue 1, pp 79–91 | Cite as

Speech synthesis for glottal activity region processing

  • Nagaraj AdigaEmail author
  • S. R. M Prasanna


The objective of this paper is to demonstrate the significance of combining different features present in the glottal activity region for statistical parametric speech synthesis (SPSS). Different features present in the glottal activity regions are broadly categorized as F0, system, and source features, which represent the quality of speech. F0 feature is computed from zero frequency filter and system feature is computed from 2-D based Riesz transform. Source features include aperiodicity and phase component. Aperiodicity component representing the amount of aperiodic component present in a frame is computed from Riesz transform, whereas, phase component is computed by modeling integrated linear prediction residual. The combined features resulted in better quality compared to STRAIGHT based SPSS both in terms of objective and subjective evaluation. Further, the proposed method is extended to two Indian languages, namely, Assamese and Manipuri, which shows similar improvement in quality.


Glottal activity region Speech synthesis Statistical parametric speech synthesis Voicing decision Riesz transform 


  1. Adiga, N., Khonglah, B. K., & Prasanna, S. M. (2017). Improved voicing decision using glottal activity features for statistical parametric speech synthesis. Digital Signal Processing, 71, 131–143.MathSciNetCrossRefGoogle Scholar
  2. Adiga, N., & Prasanna, S. R. M. (2015). Detection of glottal activity using different attributes of source information. The IEEE Signal Processing Letters, 22(11), 2107–2111.CrossRefGoogle Scholar
  3. Adiga, N. & Prasanna, S. R. M. (2018). Acoustic features modelling for statistical parametric speech synthesis: A review. IETE Technical Review.
  4. Airaksinen, M., Bollepalli, B., Juvela, L., Wu, Z., King, S. & Alku, P. (2016). Glottdnna full-band glottal vocoder for statistical parametric speech synthesis. In Proc. Interspeech.Google Scholar
  5. Alku, P. (1992). Glottal wave analysis with pitch synchronous iterative adaptive inverse filtering. Speech Communication, 1(2), 109–118.CrossRefGoogle Scholar
  6. Ananthapadmanabha, T. V. (1984). Acoustic analysis of voice source dynamics. STL-QPSR 23. Speech, Music and Hearing, Royal Institute of Technology, Stockholm: Tech. Rep.Google Scholar
  7. Aragonda, H. & Seelamantula, C. (2013) Riesz-transform-based demodulation of narrowband spectrograms of voiced speech. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., May (pp. 8203–8207).Google Scholar
  8. Aragonda, H., & Seelamantula, C. (2015). Demodulation of narrowband speech spectrograms using the Riesz transform. The IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(11), 1824–1834.CrossRefGoogle Scholar
  9. Arik, S. O., Chrzanowski, M., Coates, A., Diamos, G., Gibiansky, A., Kang, Y., Li, X., Miller, J., Raiman, J. & Sengupta, S. et al. (2017). Deep Voice: Real-time neural text-to-speech. arXiv:1702.07825.
  10. Chi, C.-Y., & Kung, J.-Y. (1995). A new identification algorithm for allpass systems by higher-order statistics. Signal Processing, 41(2), 239–256.CrossRefzbMATHGoogle Scholar
  11. De Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917–1930.CrossRefGoogle Scholar
  12. Degottex, G., & Erro, D. (2014). A uniform phase representation for the harmonic model in speech synthesis applications. EURASIP Journal on Audio Speech Music Process, 1, 1–16. Scholar
  13. Eleftherios, B., Daniel, E., Antonio, B., & Asuncion, M. (2008). Flexible harmonic/stochastic modeling for HMM-based speech synthesis. V Jornadas en Tecnologa del Habla.Google Scholar
  14. Erro, D., Sainz, I., Navas, E., & Hernaez, I. (2014). Harmonics plus noise model based vocoder for statistical parametric speech synthesis. IEEE Journal of Selected Topics in Signal Process, 8(2), 184–194.CrossRefGoogle Scholar
  15. Fisher, W. M., Doddington, G. R. & Goudie-Marshall, K. M. (1986). The DARPA speech recognition research database: Specifications and status. In Proc. DARPA workshop on speech recognition (pp. 93–99).Google Scholar
  16. Flanagan, J . L. (2013). Speech analysis, synthesis and perception (Vol. 3). New York: Springer.Google Scholar
  17. Fukada, T., Tokuda, K., Kobayashi, T., & Imai, S. (1992). An adaptive algorithm for mel-cepstral analysis of speech. Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, 1, 137–140.Google Scholar
  18. Hemptinne, C. (2006). Integration of the harmonic plus noise model (HNM) into the Hidden Markov Model-Based speech synthesis system (HTS). Master’s thesis, Idiap Research Institute.Google Scholar
  19. Hermes, D. J. (1988). Measurement of pitch by subharmonic summation. The Journal of the Acoustical Society of America, 83(1), 257–264.CrossRefGoogle Scholar
  20. Hunt, A. J., & Black, A. W. (1996). Unit selection in a concatenative speech synthesis system using a large speech database. Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, 1, 373–376.CrossRefGoogle Scholar
  21. Kawahara, H., Estill, J. & Osamu, F. (2001). Aperiodicity extraction and control using mixed mode excitation and group delay manipulation for a high quality speech analysis, modification and synthesis system straight. In Proc. MAVEBA (pp. 59–64).Google Scholar
  22. Kawahara, H., Masuda-Katsuse, I., & de Cheveign, A. (1999). Restructuring speech representations using a pitch-adaptive time frequency smoothing and an instantaneous-frequency-based F0 extraction. Speech Communication, 27, 187–207.CrossRefGoogle Scholar
  23. King, S. (2011). An introduction to statistical parametric speech synthesis. Sadhana, 36(5), 837–852.CrossRefGoogle Scholar
  24. Krishnamurthy, A., & Childers, D. (1986). Two-channel speech analysis. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(4), 730–743.CrossRefGoogle Scholar
  25. Larkin, K. G., Bone, D. J., & Oldfield, M. A. (2001). Natural demodulation of two-dimensional fringe patterns. I. General background of the spiral phase quadrature transform. The Journal of the Optical Society of America A, 18(8), 1862–1870.CrossRefGoogle Scholar
  26. Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4), 561–580.CrossRefGoogle Scholar
  27. McAulay, R. J., & Quatieri, T. F. (1986). Speech analysis/synthesis based on a sinusoidal representation. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(4), 744–754.CrossRefGoogle Scholar
  28. Mehri, S., Kumar, K., Gulrajani, I., Kumar, R., Jain, S., Sotelo, J., Courville, A. & Bengio, Y. (2016). SampleRNN: An unconditional end-to-end neural audio generation model. arXiv:1612.07837.
  29. Murthy, K. S. R., & Yegnanarayana, B. (2008). Epoch extraction from speech signals. IEEE Transactions on Audio Speech and Language Processing, 16, 1602–1613.CrossRefGoogle Scholar
  30. Murthy, K. S. R., Yegnanarayana, B., & Joseph, M. A. (2009). Characterization of glottal activity from speech signals. The IEEE Signal Processing Letters, 16(6), 469–472.CrossRefGoogle Scholar
  31. Nemer, E., Goubran, R., & Mahmoud, S. (2001). Robust voice activity detection using higher-order statistics in the LPC residual domain. IEEE Transactions on Speech and Audio Processing, 9(3), 217–231.CrossRefGoogle Scholar
  32. Oppenheim, A. V. (1969). Speech analysis-synthesis system based on homomorphic filtering. The Journal of the Acoustical Society of America, 45(2), 458–465.CrossRefGoogle Scholar
  33. Pantazis, Y. & Stylianou, Y. (2008). Improving the modeling of the noise part in the harmonic plus noise model of speech. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process, March (pp. 4609–4612).Google Scholar
  34. Patil, H. A., Patel, T. B., Shah, N. J., Sailor, H. B., Krishnan, R., Kasthuri, G., Nagarajan, T., Christina, L., Kumar, N. & Raghavendra V. et al. (2013). A syllable-based framework for unit selection synthesis in 13 Indian languages. In Proc. Oriental COCOSDA (pp. 1–8). IEEE.Google Scholar
  35. Plante, F., Meyer, G., & Ainsworth, W. (1995). A pitch extraction reference database. Children, 8(12), 30–50.Google Scholar
  36. Prathosh, A., Ananthapadmanabha, T., & Ramakrishnan, A. (2013). Epoch extraction based on integrated linear prediction residual using plosion index. IEEE Transactions on Audio Speech and Language Processing, 21(12), 2471–2480.CrossRefGoogle Scholar
  37. Quatieri, T. F. (2002). 2-D processing of speech with application to pitch estimation. In Proc. Interspeech.Google Scholar
  38. Raitio, T., Suni, A., Pulakka, H., Vainio, M. & Alku, P. (2011). Utilizing glottal source pulse library for generating improved excitation signal for HMM-based speech synthesis. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (pp. 4564–4567).Google Scholar
  39. Raitio, T., Suni, A., Yamagishi, J., Pulakka, H., Nurminen, J., Vainio, M., et al. (2011). HMM-based speech synthesis utilizing glottal inverse filtering. IEEE Transactions on Audio Speech and Language Processing, 19–1, 153–165.CrossRefGoogle Scholar
  40. Seelamantula, C. S., Pavillon, N., Depeursinge, C., & Unser, M. (2012). Local demodulation of holograms using the Riesz transform with application to microscopy. The Journal of the Optical Society of America A, 29(10), 2118–2129.CrossRefGoogle Scholar
  41. Shamma, S. (2001). On the role of space and time in auditory processing. Trends in Cognitive Sciences, 5(8), 340–348.CrossRefGoogle Scholar
  42. Sharma, B., Adiga, N. & Prasanna, S. M. (2015). Development of Assamese text-to-speech synthesis system. In Proc. TENCON (pp. 1–6). IEEE.Google Scholar
  43. Sjölander, K. & Beskow, J. (2000). Wavesurfer—An open source speech tool. In Proc. Interspeech (pp. 464–467).Google Scholar
  44. Stylianou, Y. (2001). Applying the harmonic plus noise model in concatenative speech synthesis. IEEE Transactions on Speech and Audio Processing, 9(1), 21–29.CrossRefGoogle Scholar
  45. Stylianou, I. (1996). Harmonic plus noise models for speech, combined with statistical methods, for speech and speaker modification. Ph.D. dissertation, Ecole Nationale Supérieure des TélécommunicationsGoogle Scholar
  46. Tokuda, K., Kobayashi, T., Masuko, T. & Imai, S. (1994). Mel-generalized cepstral analysis-a unified approach to speech spectral estimation. In Proceedings of ICSLP.Google Scholar
  47. Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013). Speech synthesis based on hidden Markov models. Proceedings of the IEEE, 101–5, 1234–1252.CrossRefGoogle Scholar
  48. van den oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. & Kavukcuoglu, K. (2016). WaveNet: A generative model for raw audio. arXiv:1609.03499.
  49. Wang, T., & Quatieri, T. (2012). Two-dimensional speech-signal modeling. IEEE Transactions on Audio Speech and Language Processing, 20(6), 1843–1856.CrossRefGoogle Scholar
  50. Wang, Y., Skerry-Ryan, R., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., Le, Q., Agiomyrgiannakis, Y., Clark, R. & Saurous, R. A. (2017). Tacotron: A fully end-to-end text-to-speech synthesis model. arXiv:1703.10135.
  51. Wu, Z., Watts, O., & King, S. (2016). Merlin: An open source neural network speech synthesis system. In Proceedings of the speech synthesis workshop (SSW). Sunnyvale, USA: SSW.Google Scholar
  52. Yoshimura, T., Tokuda, K., Masuko, T., Kobayashi, T. & Kitamura, T. (1999). Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis. In Proceedings of Eurospeech.Google Scholar
  53. Zen, H., Tokuda, K., & Black, A. W. (2009). Statistical parametric speech synthesis. Speech Communication, 51–11, 1039–1064.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CreteHeraklionGreece
  2. 2.Department of Electronics and Electrical EngineeringIIT GuwahatiGuwahatiIndia
  3. 3.Department of Electrical EngineeringIndian Institute of Technology DharwadDharwadIndia

Personalised recommendations