Skip to main content
SpringerLink
Log in
Book cover

International Joint Conference on Rough Sets

IJCRS 2017: Rough Sets pp 264–277Cite as

Sequential Three-Way Decisions in Efficient Classification of Piecewise Stationary SpeechSignals

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10314))

Abstract

In this paper it is proposed to improve performance of the automatic speech recognition by using sequential three-way decisions. At first, the largest piecewise quasi-stationary segments are detected in the speech signal. Every segment is classified using the maximum a-posteriori (MAP) method implemented with the Kullback-Leibler minimum information discrimination principle. The three-way decisions are taken for each segment using the multiple comparisons and asymptotical properties of the Kullback-Leibler divergence. If the non-commitment option is chosen for any segment, it is divided into small subparts, and the decision-making is sequentially repeated by fusing the classification results for each subpart until accept or reject options are chosen or the size of each subpart becomes relatively low. Thus, each segment is associated with a hierarchy of variable-scale subparts (granules in rough set theory). In the experimental study the proposed procedure is used in speech recognition with Russian language. It was shown that our approach makes it possible to achieve high efficiency even in the presence of high level of noise in the observed utterance.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://sites.google.com/site/andreyvsavchenko/SpeechDataIsolatedSyllables.zip.

  2. 2.

    https://sites.google.com/site/andreyvsavchenko/ValidationDataVowels.zip.

References

  1. Tyagi, V., Bourlard, H., Wellekens, C.: On variable-scale piecewise stationary spectral analysis of speech signals for ASR. Speech Commun. 48, 1182–1191 (2006)

    Article  Google Scholar 

  2. Savchenko, A.V., Belova, N.S.: Statistical testing of segment homogeneity in classification of piecewise-regular objects. Int. J. Appl. Math. Comput. Sci. 25, 915–925 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  3. Huang, K., Aviyente, S.: Sparse representation for signal classification. In: Advances of Neural Information Processing Systems (NIPS), pp. 609–616. MIT Press (2006)

    Google Scholar 

  4. Khan, M.R., Padhi, S.K., Sahu, B.N., Behera, S.: Non stationary signal analysis and classification using FTT transform and naive bayes classifier. In: IEEE Power, Communication and Information Technology Conference (PCITC), pp. 967–972. IEEE Press (2015)

    Google Scholar 

  5. Savchenko, A.V.: Search Techniques in Intelligent Classification Systems. Springer International Publishing, New York (2016)

    Book  MATH  Google Scholar 

  6. Benesty, J., Sondhi, M.M., Huang, Y.: Springer Handbook of Speech Processing. Springer, Berlin (2008)

    Book  Google Scholar 

  7. Peebles, P.Z., Read, J., Read, P.: Probability, Random Variables, and Random Signal Principles. McGraw-Hill, New York (2001)

    Google Scholar 

  8. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016)

    Google Scholar 

  9. Yu, D., Deng, L.: Automatic Speech Recognition: A Deep Learning Approach. Springer, New York (2014)

    MATH  Google Scholar 

  10. Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Interspeech, pp. 338–342 (2014)

    Google Scholar 

  11. Stan, A., Mamiya, Y., Yamagishi, J., Bell, P., Watts, O., Clark, R.A., King, S.: ALISA: an automatic lightly supervised speech segmentation and alignment tool. Comput. Speech Lang. 35, 116–133 (2016)

    Article  Google Scholar 

  12. Yao, Y.Y.: Granular computing and sequential three-way decisions. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 16–27. Springer, Heidelberg (2013)

    Google Scholar 

  13. Savchenko, A.V.: Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing. Knowl.-Based Syst. 91, 252–262 (2016)

    Article  Google Scholar 

  14. Li, H., Zhang, L., Huang, B., Zhou, X.: Sequential three-way decision and granulation for cost-sensitive face recognition. Knowl.-Based Syst. 91, 241–251 (2016)

    Article  Google Scholar 

  15. Yao, Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180, 341–353 (2010)

    Article  MathSciNet  Google Scholar 

  16. Yao, Y.: Interval sets and three-way concept analysis in incomplete contexts. Int. J. Mach. Learn. Cybern. 8(1), 1–18 (2017)

    Article  Google Scholar 

  17. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Norwell, MA, USA (1992)

    MATH  Google Scholar 

  18. Li, H., Zhang, L., Zhou, X., Huang, B.: Cost-sensitive sequential three-way decision modeling using a deep neural network. Int. J. Approx. Reason. 85, 68–78 (2017)

    Article  MathSciNet  Google Scholar 

  19. Li, Y., Zhang, Z.H., Chen, W.B., Min, F.: TDUP: an approach to incremental mining of frequent itemsets with three-way-decision pattern updating. Int. J. Mach. Learn. Cybern. 8(2), 441–453 (2017)

    Article  Google Scholar 

  20. Ren, R., Wei, L.: The attribute reductions of three-way concept lattices. Knowl.-Based Syst. 99, 92–102 (2016)

    Article  Google Scholar 

  21. Yao, J., Azam, N.: Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Trans. Fuzzy Syst. 23(1), 3–15 (2015)

    Article  Google Scholar 

  22. Zhang, H.R., Min, F., Shi, B.: Regression-based three-way recommendation. Inf. Sci. 378, 444–461 (2017)

    Article  Google Scholar 

  23. Li, W., Huang, Z., Li, Q.: Three-way decisions based software defect prediction. Knowl.-Based Syst. 91, 263–274 (2016)

    Article  Google Scholar 

  24. Pedrycz, W.: Granular Computing: Analysis and Design of Intelligent Systems. CRC Press, Boca Raton (2013)

    Book  Google Scholar 

  25. Wang, X., Pedrycz, W., Gacek, A., Liu, X.: From numeric data to information granules: a design through clustering and the principle of justifiable granularity. Knowl.-Based Syst. 101, 100–113 (2016)

    Article  Google Scholar 

  26. Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23, 67–72 (1975)

    Article  Google Scholar 

  27. Savchenko, A.V., Savchenko, L.V.: Towards the creation of reliable voice control system based on a fuzzy approach. Pattern Recognit. Lett. 65, 145–151 (2015)

    Article  Google Scholar 

  28. Gray, R.M., Buzo, A., Gray, J.A., Matsuyama, Y.: Distortion Measures for Speech Processing. IEEE Trans. Acoust. Speech Signal Process. 28, 367–376 (1980)

    Article  MATH  Google Scholar 

  29. Savchenko, V.V., Savchenko, A.V.: Information-theoretic analysis of efficiency of the phonetic encoding-decoding method in automatic speech recognition. J. Commun. Technol. Electron. 61, 430–435 (2016)

    Article  Google Scholar 

  30. Kullback, S.: Information Theory and Statistics. Dover Publications, New York (1997)

    MATH  Google Scholar 

  31. Marple, S.L.: Digital Spectral Analysis: With Applications. Prentice Hall, Upper Saddle River (1987)

    Google Scholar 

  32. Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., Rose, R., Tyagi, V., Wellekens, C.: Automatic speech recognition and speech variability: a review. Speech Commun. 49, 763–786 (2007)

    Article  Google Scholar 

  33. Lingras, P., Chen, M., Miao, D.: Rough multi-category decision theoretic framework. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS, vol. 5009, pp. 676–683. Springer, Heidelberg (2008). doi:10.1007/978-3-540-79721-0_90

    Chapter  Google Scholar 

  34. Zhou, B.: Multi-class decision-theoretic rough sets. Int. J. Approx. Reason. 55(1), 211–224 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  35. Ju, H.R., Li, H.X., Yang, X.B., Zhou, X.Z.: Cost-sensitive rough set: a multi-granulation approach. Knowl.-Based Syst. 123, 137–153 (2017)

    Article  Google Scholar 

  36. Deng, G., Jia, X.: A decision-theoretic rough set approach to multi-class cost-sensitive classification. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS, vol. 9920, pp. 250–260. Springer, Cham (2016). doi:10.1007/978-3-319-47160-0_23

    Chapter  Google Scholar 

  37. Liu, D., Li, T., Li, H.: A multiple-category classification approach with decision-theoretic rough sets. Fundam. Inform. 115(2–3), 173–188 (2012)

    MathSciNet  MATH  Google Scholar 

  38. Hochberg, Y., Tamhane, A.C.: Multiple Comparison Procedures. Wiley, Hoboken (2009)

    MATH  Google Scholar 

  39. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. Series B (Methodol.) 57(1), 289–300 (1995)

    MathSciNet  MATH  Google Scholar 

  40. Savchenko, A.V., Savchenko, L.V.: Classification of a sequence of objects with the fuzzy decoding method. In: Cornelis, C., Kryszkiewicz, M., Ślȩzak, D., Ruiz, E.M., Bello, R., Shang, L. (eds.) RSCTC 2014. LNCS, vol. 8536, pp. 309–318. Springer, Cham (2014). doi:10.1007/978-3-319-08644-6_32

    Google Scholar 

  41. Savchenko, A.V.: Semi-automated speaker adaptation: how to control the quality of adaptation? In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2014. LNCS, vol. 8509, pp. 638–646. Springer, Cham (2014). doi:10.1007/978-3-319-07998-1_73

    Google Scholar 

  42. Savchenko, A.V.: Phonetic words decoding software in the problem of Russian speech recognition. Autom. Remote Control 74(7), 1225–1232 (2013)

    Article  Google Scholar 

  43. Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., Silovsky, J.: The kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. IEEE Signal Processing Society (2011)

    Google Scholar 

  44. Gillick, L., Cox, S.: Some statistical issues in the comparison of speech recognition algorithms. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 532–535 (1989)

    Google Scholar 

Download references

Acknowledgements.

The work was prepared within the framework of the Academic Fund Program at the National Research University Higher School of Economics (HSE) in 2017 (grant No 17-05-0007) and is supported by the Russian Academic Excellence Project “5–100” and Russian Federation President grant no. MD-306.2017.9.

Author information

Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russian Federation

    Andrey V. Savchenko

Authors
  1. Andrey V. Savchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrey V. Savchenko .

Editor information

Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Savchenko, A.V. (2017). Sequential Three-Way Decisions in Efficient Classification of Piecewise Stationary SpeechSignals. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10314. Springer, Cham. https://doi.org/10.1007/978-3-319-60840-2_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60840-2_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60839-6

  • Online ISBN: 978-3-319-60840-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation