Skip to main content
SpringerLink
Log in
Book cover

Embedded Systems and Artificial Intelligence pp 281–289Cite as

Pathological Detection Using HMM Speech Recognition-Based Amazigh Digits

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1076))

Abstract

In the last decade, the automatic speech pathology detection systems based on voice production theory are evolving up to date. Overall, there have not been much speech technology research studies for persons regarding voice disorders which center on Amazigh language. This research project focuses on the build of an automatic speech recognition system based on Sphinx-4 that permits to detect the differences between normal and pathological voices based on the produced speech. The performance in our system was measured using the combinations of different Hidden Markov Models and Gaussian mixture distributions. Results show that the maximum accuracy with the normal voices is greater than the maximum accuracy obtained from the pathological speaker.

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   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

References

  1. Halton, M., Cerisara, C., Fohr, D., Laprie, Y., Smaili, K.: Reconnaissance automatiue de la parole du signal a son interpretation. Monographies and Books, Oxford (2006)

    Google Scholar 

  2. Satori, H., Hiyassat, H., Harti, M., Chenfour, N.: Investigation Arabic speech recognition using CMU sphinx system. Int. Arab J. Inf. Technol. 6(2) (2009)

    Google Scholar 

  3. O’Shaughnessy, D.: Automatic speech recognition: history, methods and challenges. Pattern Recogn. 41(10), 2965–2979 (2008)

    Article  Google Scholar 

  4. Satori, H., Zealouk, O., Satori, H., et al.: Voice comparison between smokers and non-smokers using HMM speech recognition system. Int. J. Speech Technol. 20(4), 771–777 (2017)

    Article  Google Scholar 

  5. Dubuisson, T., Dutoit, T., Gosselin, B., Remacle, M.: On the use of the correlation between acoustic descriptors the normal/pathological voices discrimination. EURASIP J. Adv. Signal Process. 2009(1), 173967 (2009)

    Article  Google Scholar 

  6. Michaelis, D., Frohlich, M., Strube, H.W.: Selection and combination of acoustic features for the description of pathologic voices. J. Acoust. Soc. Am. 103(3), 1628–1639 (1998). https://doi.org/10.1121/1.421305

    Article  Google Scholar 

  7. Lieberman, P.: Perturbation in vocal pitch. J. Acoust. Soc. 33(5), 597–603 (1961)

    Article  Google Scholar 

  8. Vasilakis, M., Stylianou, Y.: Sepctral jitter modeling and estimation. Biomed. Signal Process. Control 4(3), 183–193 (2009)

    Article  Google Scholar 

  9. Muhammad, G., Mesallam, T.A., Malki, K.H., Farahat, M., Alsulaiman, M., Bukhari, M.: Formant analysis in dysphonic patients and automatic Arabic digit speech recognition. Biomed. Eng. Online 10(41), 1–12 (2011)

    Google Scholar 

  10. Godino-Llorente, J., Gomez-Vilda, P., Blanco-Velasco, M.: Dimensionally reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE Trans. Biomed. Eng. 53(10), 1943–1953 (2006)

    Article  Google Scholar 

  11. Wiśniewski, M., Kuniszyk-Jóźkowiak, W., Smołka, E., Suszyński, W.: Automatic detection of disorders in a continuous speech with the Hidden Markov Models approach. In: Computer Recognition Systems 2, vol. 45, pp. 447–453 (2007)

    Google Scholar 

  12. Muhammad, G., Alsulaiman, M., Ali, Z., Mesallam, T.A., Farahat, M., Malki, K.H., Al-nasheri, A., Bencherif, M.A.: Voice pathology detection using interlaced derivative pattern on glottal source excitation. Biomed. Signal Process. Control 31, 156–164 (2017)

    Google Scholar 

  13. Woldert-Jokisz, Bogdan. Saarbruecken Voice Database. (2007)

    Google Scholar 

  14. Gaikwad, S.K., Gawali, B.W., Yannawar, P.: A review on speech recognition technique. Int. J. Comput. Appl. 10(3), 16–24 (2010)

    Google Scholar 

  15. Huang, X., Acero, A., Hon, H., Foreword, B.: Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Prentice Hall PTR (2001)

    Google Scholar 

  16. Boukous, A.: Société, langues et cultures au Maroc: Enjeux symboliques. Najah El Jadida, Casablanca, Maroc, 1f895

    Google Scholar 

  17. Ouakrim, O.: Fonética y fonología del Bereber. Survey: University of Autònoma de Barcelona (1995)

    Google Scholar 

  18. Chaker, S.: Textes en linguistique berbère: introduction au domaine berbère. Ed. du C.N.R.S, Paris (1984)

    Google Scholar 

  19. Ridouane, R.: Suites de consonnes en berbère: phonétiqueet phonologie. Doctoral Dissertation, Université de la Sorbonne nouvelle-Paris III (2003)

    Google Scholar 

  20. Mohamed, H., Hassan, S., Ouissam, Z., Khalid, S., Naouar, L.: Interactive voice response server voice network administration using Hidden Markov Model speech recognition system. In: Second World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pp. 16–21. IEEE (2018, October)

    Google Scholar 

  21. Zealouk, O., Satori, H., Hamidi, M., Satori, K.: Voice pathology assessment based on automatic speech recognition using Amazigh digits. In: Proceedings of the 2nd International Conference on Smart Digital Environment, pp. 100–105. ACM (2018, October)

    Google Scholar 

  22. Hamidi, M., Satori, H., Satori, K.: Implementing a voice interface in VOIP network with IVR server using Amazigh digits. Int. J. Multi. Sci. 2, 38–43 (2016)

    Google Scholar 

  23. Zealouk, O., Satori, H., Hamidi, M., Laaidi, N., Satori, K.: Vocal parameters analysis of smoker using Amazigh language. Int. J. Speech Technol. 21(1), 85–91 (2018)

    Article  Google Scholar 

  24. Hamidi, M., Satori, H., Zealouk, O., Satori, K.: Speech coding effect on Amazigh alphabet speech recognition performance. J. Adv. Res. Dyn. Control Syst. 11(2), 1392–1400 (2019)

    Google Scholar 

  25. Zealouk, O., Satori, H., Hamidi, M., Satori, K.: Speech recognition for Moroccan dialects: feature extraction and classification methods. J. Adv. Res. Dyn. Control Syst. 11(2), 1401–1408 (2019)

    Google Scholar 

  26. Young, S., Evermann, G., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The Book (2002). http://htk.eng.cam.ac.uk

  27. Carnegie Mellon University. Sphinx-4. Available http://cmusphinx.sourceforge.net

  28. Satori, H., Harti, M., Chenfour, N.: Arabic speech recognition system using cmu-sphinx4. arXiv preprint (2007). arXiv:0704.2201

  29. Varela, A., Cuayáhuitl, H., Nolazco-Flores, J.A.: Creating a Mexican Spanish Version of the CMU Sphinx-III Speech Recognition System, vol. 2905. Springer (2003)

    Google Scholar 

  30. Zulfiqar, A., Alsulaiman, M., Elmavazuthi, I., et al.: Voice pathology detection based on the modified voice contour and SVM. Biol. Inspired Cogn. Archit. 15, 10–18 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory Computer Science, Image Processing and Numerical Analysis, Faculty of Sciences Dhar Mahraz, Sidi Mohamed Ben Abdellah University, Fés, Morocco

    Ouissam Zealouk, Hassan Satori, Mohamed Hamidi & Khalid Satori

Authors
  1. Ouissam Zealouk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hassan Satori
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Hamidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Khalid Satori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ouissam Zealouk .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  3. Department of Computer Sciences, Faculty of Sciences Dhar Mahraz, Sidi Mohammed Ben Abbdallah University, Fez, Morocco

    Hassan Satori

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zealouk, O., Satori, H., Hamidi, M., Satori, K. (2020). Pathological Detection Using HMM Speech Recognition-Based Amazigh Digits. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_27

Download citation

Publish with us

Policies and ethics

Search

Navigation