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Automatic speech signal segmentation based on the innovation adaptive filter Cover

Automatic speech signal segmentation based on the innovation adaptive filter

Open Access
|Jun 2014

References

  1. Almpanidis, G. and Kotropoulos, C. (2007). Phonetic segmentation using the generalized Gamma distribution and small sample Bayesian information criterion,(1): 38–55.
  2. Almpanidis, G., Kotti, M. and Kotropoulos, C. (2009). Robust detection of phone boundaries using model selection criteria with few observations,(2): 287–298.
  3. Barkat, M. (1991)., Artech House, Boston, MA.
  4. Brandt, A.V. (1983). Detecting and estimating the parameters jumps using ladder algorithms and likelihood ratio test,, pp. 1017–1020.
  5. Brugnara, F., Falavinga, D. and Omolongo, M. (1993). Automatic segmentation and labeling of speech based on hidden Markov models,(4): 357–370.
  6. Delacourt, P. and Wellekens, C.J. (2000). DISTBIC: A speaker-based segmentation for audio data indexing,(1–2): 111–126.
  7. Gomez, J.A. and Calvo, M. (2011). Improvements on automatic speech segmentation at the phonetic level,C. San Martin and S.-W. Kim (Eds.),, Lecture Notes in Computer Science, Vol. 7042, Springer-Verlag, Berlin/Heidelberg, pp. 557–564.
  8. Haykin, S. (1996)., Prentice-Hall, Englewood Cliffs, NJ.
  9. Jamouli, H., Al Hail, M.A. and Sauter, D. (2012). A mixed active and passive GLR test for a fault tolerant control system,(1): 9–23, DOI: 10.2478/v10006-012-0001-1.
  10. Kay, S.M. (1988)., Prentice-Hall, Englewood Cliffs, NJ.
  11. Kay, S.M. (1998)., Prentice-Hall, Englewood Clifft, NJ.
  12. Kroon, P. and Deprettere, E.F. (1988). A class of analysis-by-synthesis predictive coders for high quality speech coding at rates between 4.8 and 16 kbits/s,(2): 353–363.
  13. Lee, D.T.L., Morf, M. and Friedlander, B. (1981). Recursive least squares ladder estimation algorithms,(6): 627–641.
  14. Lopatka, M., Adam, O., Laplanche, C., Zarzycki, J. and Motsch, J-F. (2005). Effective analysis of non-stationary short-time signals based on the adaptive Schur filter,, pp. 251–256.
  15. Lopatka, M., Adam, O., Laplanche, C., Motsch, J-F. and Zarzycki, J. (2006). Sperm whale click analysis using a recursive time-variant lattice filter,(11–12): 1118–1133.
  16. Makowski, R. and Zimroz, R. (2013). A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings,(1): 65–77.
  17. Mporas, I., Ganchev, T. and Fakotakis, N. (2008). Phonetic segmentation using multiple speech features,(1): 73–85.
  18. Park, S.S. and Kim, N.S. (2007). On using multiple models for automatic speech segmentation,(8): 2202–2212.
  19. Prasad, V.K., Nagarajan, T. and Murthy, H.A. (2004). Automatic segmentation of continuous speech using minimum phase delay functions,(3–4): 429–446.
  20. Puig, V. (2010). Fault diagnosis and fault tolerant control using set-membership approaches: Application to real case studies,(4): 619–635, DOI: 10.2478/v10006-010-0046-y.
  21. Rabiner, L. and Gold, B. (1975)., Prentice-Hall, Englewood Cliffs, NJ.
  22. Rabiner, L. and Juang, B-H. (1993)., Prentice-Hall, Englewood Cliffs, NJ.
  23. Rudoy, D., Quatieri, T.F. and Wolfe, P.J. (2011). Time-varying autoregressions in speech: Detection theory and applications,(4): 977–989.
  24. Scharenborg, O., Wan, V. and Ernestus, M. (2010). Unsupervised speech segmentation: An analysis of the hypothesized phone boundaries,(2): 1084–1095.
  25. Schwarz, P., Matejka, P. and Cernocky, J. (2006). Hierarchical structures of neural networks for phoneme recognition,Vol. 1, pp. 325–328.
  26. Sharma, M. and Mammone, R. (1996). Blind speech segmentation: Automatic segmentation of speech without linguistic knowledge,pp. 1237–1240.
  27. Toledano, D.T., Hernandez Gomez, L.A. and Villarrubia Grande, L. (2003) Automatic phonetic segmentation,(6): 617–625.
  28. Tyagi, V., Bourlard, H. and Wellekens, C. (2006). On variable-scale piecewise stationary analysis of speech signals for ASR,(9): 1182–1191.
DOI: https://doi.org/10.2478/amcs-2014-0019 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 259 - 270
Submitted on: Jan 21, 2013
Published on: Jun 26, 2014
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2014 Ryszard Makowski, Robert Hossa, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.