Automatic Speech Segmentation Based on Acoustical Clustering

  • Jon A. Gómez
  • Emilio Sanchis
  • María J. Castro-Bleda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)


In this paper, we present an automatic speech segmentation system based on acoustical clustering plus dynamic time warping. Our system operates at three stages, the first one obtains a coarse segmentation as a starting point to the second one. The second stage fixes phoneme boundaries in an iterative process of progressive refinement. The third stage makes a finer adjustment by considering some acoustic parameters estimated at a higher subsampling rate around the boundary to be adjusted. No manually segmented utterances are used in any stage.

The results presented here demonstrate a good learning capability of the system, which only uses the phonetic transcription of each utterance. Our approach obtains similar results than the ones reported by previous related works on TIMIT database.


automatic speech segmentation phoneme boundaries detection phoneme alignment 


  1. 1.
    Toledano, D.T., Hernández Gómez, L., Villarrubia Grande, L.: Automatic Phonetic Segmentation. IEEE Transactions on Speech and Audio Processing 11(6), 617–625 (2003)CrossRefGoogle Scholar
  2. 2.
    Kipp, A., Wesenick, M.B., Schiel, F.: Pronunciation modelling applied to automatic segmentation of spontaneous speech. In: Proceedings of Eurospeech, Rhodes, Greece, pp. 2013–2026 (1997)Google Scholar
  3. 3.
    Adell, J., Bonafonte, A., Gómez, J.A., Castro, M.J.: Comparative study of automatic phone segmentation methods for TTS. In: IEEE ICASSP, Philadelphia, USA, vol. 1, pp. 309–312 (2005)Google Scholar
  4. 4.
    Pikrakis, A., Giannakipoulos, T., Theodoridis, S.: A Speech/Music Discriminator of Radio Recordings Based on Dynamic Programming and Bayesian Networs. IEEE Trans. on Multimedia 10, 846–857 (2008)CrossRefGoogle Scholar
  5. 5.
    Gómez, J.A., Castro, M.J.: Automatic Segmentation of Speech at the Phonetic Level. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 672–680. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  7. 7.
    Mporas, I., Ganchev, T., Fakotakis, N.: A Hybrid Architecture for Automatic Segmentation of Speech Waveforms. In: IEEE ICASSP 2008, Las Vegas, USA, pp. 4457–4460 (2008)Google Scholar
  8. 8.
    Moreno, A., Poch, D., Bonafonte, A., Lleida, E., Llisterri, J., Mariño, J.B., Nadeu, C.: Albayzin Speech Database: Design of the Phonetic Corpus. In: Eurospeech 1993, Berlin, Germany, September 1993, vol. 1, pp. 653–656 (1993)Google Scholar
  9. 9.
    TIMIT Acoustic-Phonetic Continuous Speech Corpus, National Institute of Standards and Technology Speech Disc 1-1.1, NTIS Order No. PB91-5050651996 (October 1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jon A. Gómez
    • 1
  • Emilio Sanchis
    • 1
  • María J. Castro-Bleda
    • 1
  1. 1.Departamento de Sistemas Informáticos y ComputaciónUniversidad Politécnica de ValenciaSpain

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