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Automatic boundary detection based on entropy measures for text-independent syllable segmentation

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Abstract

In this paper, we study the boundary detection in syllable segmentation field. We describe an algorithm proposed for text-independent syllable segmentation. This algorithm provides a performance comparison between the entropies of Shannon, Tsallis and Renyi in an effective detection of beginning-ending points of syllable in a speech signal. The Shannon generalizations (Tsallis and Renyi) quantify the degree of signal organization and offer the relevant information such as the voicing degree on the first syllable segment that we obtained from the temporal dynamics of singularity exponents. The method we propose is focused on an aggregation measure based on entropies to enhance the syllable boundaries detection. It has been also demonstrated in this paper that the best suited entropy for efficient boundary detection is Renyi entropy. Once evaluated, our algorithm produced better performance with efficient results on two languages, i.e., the Fongbe (an African tonal language spoken especially in Benin, Togo, and Nigeria) and an American English. The overall accuracy of syllable boundaries was obtained on Fongbe dataset and validated subsequently on TIMIT dataset with a margin of error < 5m s.

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Notes

  1. www.fongbe.fr

References

  1. Baraniuk R, Flandrin P, Janssen A, Michel O (2001) Measuring time-frequency information content using the renyi entropies. In: IEEE Transactions on Information Theory, Vol. 47, IEEE, pp 1391– 1409

  2. Boashash B Time frequency signal analysis and processing: A comprehensive reference. In: Elsevier, Oxford, Elsevier, p 2003

  3. Chen X, Qiu X, Zhu C, Liu P, Huang X (2015) Long short-term memory neural networks for chinese word segmentation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp 1197–1206

  4. Ching-Tang H, Mu-Chun S, Eugene L, Chin H (1999) A segmentation method for continuous speech utilizing hybrid neuro-fuzzy network. J Inf Sci Eng 15 (4):615–628

    Google Scholar 

  5. Chou C-H, Liu P-H, Cai B (2008) On the studies of syllable segmentation and improving mfccs for automatic birdsong recognition. In: Asia-Pacific Services Computing Conference, IEEE, pp 745– 750

  6. Demeechai T, Makelainen K (2001) Recognition of syllables in a tone language. Speech Comm, Elsevier 33(3):241–254. doi:10.1016/S0167-6393(00)00017-0

    Article  MATH  Google Scholar 

  7. Fantinato PC, Guido RC, Chen S.-H., Santos BLS, Vieira LS, J SB, Rodrigues LC, Sanchez F, Escola J, Souza LM, Maciel CD, Scalassara PR, Pereira J (2008) A fractal-based approach for speech segmentation. In: Tenth IEEE International Symposium on Multimedia, IEEE Computer Society, pp 551–555

  8. Graves A, Fernn̈dez S., Gomez F, Schmidhuber J (2006) Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In: ICML, Pittsburgh, USA, pp 369–376

  9. Haque MA, Kim J-M (2011) An enhanced fuzzy c-means algorithm for audio segmentation and classification. Multimedia Tools Appl 63(2):485–500. doi:10.1007/s11042-011-0921-z

  10. Howitt A (2002) Vowel landmark detection. J Acoust Soc Am. 112(5):2279. doi:10.1121/1.4779139

  11. Jittiwarangkul N, Jitapunkul S, Luksaneeyanavin S, Ahkuputra V, Wutiwiwatchai C (169) Thai syllable segmentation for connected speech based on energy. In: The Asia-Pacific Conference on Circuits and Systems, IEEE

  12. Khanagha V, Daoudi K, Pont O, Yahia H (2011) Improving text-independent phonetic segmentation based on the microcanonical multiscale formalism. In: IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, pp 4484–4487

  13. Khanagha V, Daoudi K, Pont O, Yahia H (2014) Phonetic segmentation of speech signal using local singularity analysis. Digital Signal Process Elsevier 35:86–94. doi:10.1016/j.dsp.2014.08.002

  14. Kinsner W, Grieder W (2008) Speech segmentation using multifractal measures and amplification of signal features. In: 7th International Conference on Cognitive Informatics, IEEE Computer Society, pp 351–357

  15. Landsiedel C, Edlund J, Eyben F, Neiberg D, Schuller B (2011) Syllabification of conversational speech using bidirectional long-short-term memory neural networks. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 5256–5259. doi:10.1109/ICASSP.2011.5947543

  16. Makashay M, Wightman C, Syrdal A, Conkie A (2000) Perceptual evaluation of automatic segmentation in text-to-speech synthesis. In: Proceedings of the 6th conference of spoken and language processing, Beijing, China

  17. Mermelstein P (1957) Automatic segmentation of speech into syllabic units. J Acoust Soc Am 58:880–883

    Article  Google Scholar 

  18. Obin N, Lamare F, Roebel A (2013) Syll-o-matic: an adaptive time-frequency representation for the automatic segmentation of speech into syllables. In: International conference on acoustics, Speech and Signal Processing, IEEE, pp 6699–6703

  19. Origlia A, Cutugno F, Galat V (2014) Continuous emotion recognition with phonetic syllables. Speech Comm 57:155–169. doi:10.1016/j.specom.2013.09.012

  20. Pan F, Ding N (2010) Speech denoising and syllable segmentation based on fractal dimension. In: International Conference on Measuring Technology and Mechatronics Automation, IEEE, pp 433–436

  21. Petrillo M, Cutugno F (2003) A syllable segmentation algorithm for english and italian. In: Proceedings of 8th european conference on speech communication and technology, EUROSPEECH, Geneva, pp 2913–2916

  22. Pfitzinger H, Burger S, Heid S (1996) Syllable detection in read and spontaneous speech. In: Proceedings of the Fourth International Conference on Spoken Language (ICSLP), Vol. 2, IEEE, pp 1261– 1264

  23. Pikrakis A, Giannakopoulos T, Theodoridis S (2008) An overview of speech/music discrimination techniques in the context of audio recordings. In: Multimedia Services in Intelligent Environments, Springer Berlin Heidelberg, pp 81–102

  24. Prasad VK, Nagarajan T, Murthy HA (2004) Automatic segmentation of continuous speech using minimum phase group delay functions. Speech Comm 42(3-4):429–446. doi:10.1016/j.specom.2003.12.002

  25. Pont O, Turiel A, Yahia H (2011) An optimized algorithm for the evaluation of local singularity exponents in digital signals. In: Combinatorial Image Analysis, Springer Berlin Heidelberg, pp 346– 357

  26. Rasanen O, Laine U, Altosaar T (2009) An improved speech segmentation quality measure: the r-value. In: Proceedings of INTERSPEECH, pp 1851–1854

  27. Renyi A On measures of entropy and information. In: Proceedings of the fourth berkeley symposium on mathematical statistics and probability, Vol. 1, University of California Press, Berkeley, Calif, 1961, pp. 547–561

  28. Saunders J (1996) Real-time discrimination of broadcast speech/music. In: Proceedings of the Acoustics, Speech, and Signal Processing, pp 993–996

  29. Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27:379– 423

    Article  MathSciNet  MATH  Google Scholar 

  30. Shastri L, Chang S, Greenberg S (1999) Syllable detection and segmentation using temporal flow neural networks. In: Proceedings of the Fourteenth International Congress of Phonetic Sciences, pp 1721– 1724

  31. Sheikhi G, Farshad A (2011) Segmentation of speech into syllable units using fuzzy smoothed short term energy contour. In: Proceedings of international conference on acoustics, Speech and Signal Processing, IEEE, pp 195–198

  32. Shen HJE, Lee JL (1998) Robust entropy-based endpoint detection for speech recognition in noisy environments. In: Fifth international conference on spoken language processing

  33. Sreekumar K, George K, Arunraj K, Kumar C (2014) Spectral matching based voice activity detector for improved speaker recognition. In: International conference on power signals control and computations, EPSCICON, IEEE, pp 1–4

  34. Tsallis C (1998) Possible generalization of boltzmann-gibbs statistics. J Stat Phys 52(1-2):479– 487

    Article  MathSciNet  MATH  Google Scholar 

  35. Turiel A, Parga N (2000) The multi-fractal structure of contrast changes in natural images: from sharp edges to textures. Neural Comput 12:763–793

    Article  Google Scholar 

  36. Turiel A, Prez-Vicente C, Grazzini J (2006) Numerical methods for the estimation of multifractal singularity spectra on sampled data: A comparative study. J Comput Phys 216(1):362–390. doi:10.1016/j.jcp.2005.12.004

  37. Villing R, Timoney J, Ward T, Costello J (2004) Automatic blind syllable segmentation for continuous speech. In: Proceedings of the irish signals and systems conference, Belfast, UK, pp 41–46

  38. Vuuren VZ, Bosch L, Niesler T Unconstrained speech segmentation using deep neural networks. In: ICPRAM 2015 - Proceedings of the international conference on pattern recognition applications and methods, lisbon, Portugal, Vol. 1

  39. Wu L, Shire M, Greenberg S, Morgan N (1997) Integrating syllable boundary information into speech recognition. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing, Vol. 2, IEEE, pp 987–990

  40. Yahia H, Sudre J, Pottier C, Garcon V (2010) Motion analysis in oceanographic satellite images using multiscale methods and the energy cascade. J Pattern Recognit 43(10):3591–3604. doi:10.1016/j.patcog.2010.04.011

  41. Zhao X, O’Shqughnessy D (2008) A new hybrid approach for automatic speech signal segmentation using silence signal detection, energy convex hull, and spectral variation. In: Canadian Conference on Electrical and Computer Engineering, IEEE, pp 145–148

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Authors and Affiliations

  1. Laboratoire d’Informatique Signal et Image de la Côte d’Opale, Université du Littoral Côte d’Opale, 50 rue F. Buisson, BP 719, 62228, Calais Cedex, France

    Fréjus A. A. Laleye & Cina Motamed

  2. Institut de Mathématiques et de Sciences Physiques, Université d’Abomey-Calavi, BP 613, Porto-Novo, Bénin

    Eugène C. Ezin

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  1. Fréjus A. A. Laleye
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Laleye, F.A.A., Ezin, E.C. & Motamed, C. Automatic boundary detection based on entropy measures for text-independent syllable segmentation. Multimed Tools Appl 76, 16347–16368 (2017). https://doi.org/10.1007/s11042-016-3911-3

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