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A REVIEW: METHODS OF AUTOMATIC SPEECH SEGMENTATION

https://doi.org/10.55452/1998-6688-2021-18-3-89-94

Abstract

Segmentation is a process of dividing a speech signal into the basic units of language. Segmentation of the speech signals is one of the most important tasks in automatic speech processing systems. This paper proposes a review of methods of automatic speech segmentation. Moreover, methods of wavelet and Hilbert-Huang transformations and techniques based on hidden Markov models are considered.

About the Authors

A. A. Pak
Institute of Information and Computational technologies
Kazakhstan

050000, st. Pushkin 125b, Almaty



A. Zhumageldikyzy
Kazakh-British technical university
Kazakhstan

050000, st. Tole bi 59, Almaty



N. S. Ermekova
Zhetysu university named after I.Zhansugurov
Kazakhstan

040000, st. I. Zhansugurov 187a, Taldykorgan



References

1. A. I. Topnikov. BBK 387-013ya73 T58 Rekomendovano Redakcionno-izdatel'skim sovetom universiteta v kachestve uchebnogo izdaniya. Plan 2018 goda. – 2018.

2. A. K. Alimuradov, P. P. Churakov. Obzor i klassifikaciya metodov obrabotki rechevyh signalov v sistemah raspoznavaniya rechi //Izmerenie. Monitoring. Upravlenie. Kontrol'. – 2015. – №. 2 (12).

3. D. Jones. et al. Measuring human readability of machine-generated text: three case studies in speech recognition and machine translation //Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. – IEEE, 2005. – Т. 5. – С. v/1009-v/1012 Vol. 5.

4. Hant E. Iskusstvennyj intellekt. 1978 g.- 558 s

5. A. E. Sakran. et al. A Review: Automatic Speech Segmentation //International Journal of Computer Science and Mobile Computing. – 2017. – Т. 6. – №. 4. – С. 308-315.

6. K. Geetha, R. Vadivel. Phoneme Segmentation of Tamil Speech Signals Using Spectral Transition Measure //Oriental Journal of Computer Science and Technology. – 2017. – Т. 10. – №. 1. – С. 114-119.

7. O. A. Vishnyakova, D. N. Lavrov. Avtomaticheskaya segmentaciya rechevogo signala na baze diskretnogo vejvlet-preobrazovaniya //Matematicheskie struktury i modelirovanie. – 2011. – №. 2 (23).

8. I. Daubechies. Ten lectures on wavelets. – Society for industrial and applied mathematics, 1992.

9. D. Wang, S. Narayanan. Piecewise linear stylization of pitch via wavelet analysis //Ninth European Conference on Speech Communication and Technology. – 2005.

10. K. K. Tomchuk. Segmentaciya rechevyh signalov dlya zadach avtomaticheskoj obrabotki rechi : dis. – S.-Peterb. gos. un-t aerokosm. priborostroeniya, 2017.

11. G. Tzanetakis, G. Essl, P. Cook. Audio analysis using the discrete wavelet transform //Proc. Conf. in Acoustics and Music Theory Applications. – 2001. – Т. 66.

12. J. I. Agbinya. Discrete wavelet transform techniques in speech processing //Proceedings of Digital Processing Applications (TENCON'96). – IEEE, 1996. – Т. 2. – С. 514-519.

13. S. Ratsameewichai. et al. Thai phoneme segmentation using dual-band energy contour // Proceedings of the IEEK Conference. – The Institute of Electronics and Information Engineers, 2002. – С. 110-112.

14. B. Ziółko. et al. Wavelet method of speech segmentation //2006 14th European Signal Processing Conference. – IEEE, 2006. – С. 1-5.

15. Yu. E. Ul'yanova, R. G. Babenko, A. V. Chernov. Chastotno-vremennye preobrazovaniya, ispol'zuemye v cifrovoj obrabotke signalov //Global'naya yadernaya bezopasnost'. – 2015. – №. 3 (16).

16. N. E. Huang. et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis //Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences. – 1998. – Т. 454. – №. 1971. – С. 903-995.

17. E. Huang. Huang. Hilbert-Huang Transform and its application. Interdisciplinary mathematical sciences / E. Huang. Huang, S. P. Samuel. Shen // Interdisciplinary Mathematical Sciences. Book 5. World Scientific Publishing Company. – Sep. 2005. – 324 p.

18. J. Dines, S. Sridharan, M. Moody. Automatic speech segmentation with hmm //Proceedings of the 9th Australian Conference on Speech Science and Technology. – 2002. – С. 544-549.

19. A. Stolcke. et al. Highly accurate phonetic segmentation using boundary correction models and system fusion //2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). – IEEE, 2014. – С. 5552-5556.


Review

For citations:


Pak A.A., Zhumageldikyzy A., Ermekova N.S. A REVIEW: METHODS OF AUTOMATIC SPEECH SEGMENTATION. Herald of the Kazakh-British Technical University. 2021;18(3):89-94. https://doi.org/10.55452/1998-6688-2021-18-3-89-94

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ISSN 1998-6688 (Print)
ISSN 2959-8109 (Online)