Automatic Phonetic Segmentation and Pronunciation Detection with Various Approaches of Acoustic Modeling

  • Petr Mizera
  • Petr PollakEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11096)


The paper describes HMM-based phonetic segmentation realized by KALDI toolkit with the focus on study of accuracy of various acoustic modeling such as GMM-HMM vs. DNN-HMM, monophone vs. triphone, speaker independent vs. speaker dependent. The analysis was performed using TIMIT database and it proved the contribution of advanced acoustic modeling for the choice of a proper pronunciation variant. For this purpose, the lexicon covering the pronunciation variability among TIMIT speakers was created on the basis of phonetic transcriptions available in TIMIT corpus. When the proper sequence of phones is recognized by DNN-HMM system, more precise boundary placement can be then obtained using basic monophone acoustic models.


Automatic phonetic segmentation Pronunciation variability GMM-HMM DNN-HMM KALDI TIMIT 



The research described in this paper was supported by internal CTU grant SGS17/183/OHK3/3T/13 “Special Applications of Signal Processing”.


  1. 1.
    CMUSphinx: Open source speech recognition toolkit.
  2. 2.
    Brunet, R.G., Murthy, H.A.: Pronunciation variation across different dialects for English: a syllable-centric approach. In: 2012 National Conference on Communications (NCC) (2012)Google Scholar
  3. 3.
    Garofolo, J.S., et al.: TIMIT Acoustic-Phonetic Continuous Speech Corpus LDC93S1. Web download. Linguistic Data Consortium, Philadelphia (1993)CrossRefGoogle Scholar
  4. 4.
    Ghoshal, A., Povey, D.: Sequence-discriminative training of deep neural networks. In: Proceedings of the INTERSPEECH, Lyon, France (2013)Google Scholar
  5. 5.
    Kahn, A., Steiner, I.: Qualitative evaluation and error analysis of phonetic segmentation. In: 28. Konferenz Elektronische Sprachsignalverarbeitung, Saarbrücken, Germany, pp. 138–144 (2017)Google Scholar
  6. 6.
    Lee, K.F., Hon, H.W.: Speaker-independent phone recognition using hidden Markov models. IEEE Trans. Audio Speech Lang. Process. 37(11), 1641–1648 (1989)CrossRefGoogle Scholar
  7. 7.
    Matoušek, J., Klíma, M.: Automatic phonetic segmentation using the KALDI toolkit. In: Ekštein, K., Matoušek, V. (eds.) TSD 2017. LNCS (LNAI), vol. 10415, pp. 138–146. Springer, Cham (2017). Scholar
  8. 8.
    Matoušek, J., Tihelka, D., Psutka, J.: Experiments with automatic segmentation for Czech speech synthesis. In: Matoušek, V., Mautner, P. (eds.) TSD 2003. LNCS (LNAI), vol. 2807, pp. 287–294. Springer, Heidelberg (2003). Scholar
  9. 9.
    Mizera, P., Pollak, P., Kolman, A., Ernestus, M.: Impact of irregular pronunciation on phonetic segmentation of Nijmegen corpus of casual Czech. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2014. LNCS (LNAI), vol. 8655, pp. 499–506. Springer, Cham (2014). Scholar
  10. 10.
    Nouza, J., Silovský, J.: Adapting lexical and language models for transcription of highly spontaneous spoken Czech. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2010. LNCS (LNAI), vol. 6231, pp. 377–384. Springer, Heidelberg (2010). Scholar
  11. 11.
    Peddinti, V., Wang, Y., Povey, D., Khudanpur, S.: Low latency acoustic modeling using temporal convolution and LSTMs. IEEE Signal Process. Lett. 25(3), 373–377 (2018)CrossRefGoogle Scholar
  12. 12.
    Povey, D., et al.: The Kaldi speech recognition toolkit. In: Proceedings of the IEEE 2011 Workshop on Automatic Speech Recognition and Understanding, ASRU 2011 (2011)Google Scholar
  13. 13.
    Rendel, A., Sorin, A., Hoory, R., Breen, A.: Toward automatic phonetic segmentation for TTS. In: Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, pp. 4533–4536 (2012)Google Scholar
  14. 14.
    Rybach, D., et al.: The RWTH Aachen university open source speech recognition system. In: Proceedings of Interspeech 2009 (2009)Google Scholar
  15. 15.
    Stolcke, A., Ryant, N., Mitra, V., Yuan, J., Wang, W., Liberman, M.: Highly accurate phonetic segmentation using boundary correction models and system fusion. In: Proceedings of ICASSP, Florence, Italy (2014)Google Scholar
  16. 16.
    Toledano, D.T., Gómez, L.A.H., Grande, L.V.: Automatic phoneme segmentation. IEEE Trans. Speech Audio Process. 11(6), 617–625 (2003)CrossRefGoogle Scholar
  17. 17.
    Young, S., et al.: The HTK Book, Version 3.4.1. Cambridge (2009)Google Scholar
  18. 18.
    Yuan, J., Ryant, N., Liberman, M., Stolcke, A., Mitra, V., Wang, W.: Automatic phonetic segmentation using boundary models. In: Proceedings of INTERSPEECH, Lyon, France, pp. 2306–2310 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringCzech Technical University in PraguePraha 6Czech Republic

Personalised recommendations