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Enhancing Amazigh Speech Recognition System with MFDWC-SVM

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Computational Science and Its Applications – ICCSA 2023 (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13956 ))

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Abstract

In this research, a new Automatic Speech Recognition (ASR) system is introduced for the Amazigh language, more precisely Tachelhit, spoken in the High Atlas and the South-West of Morocco. The implementation of this system is a challenging task because the availability of Amazigh speech databases is limited, so the first step is to create a database and then to develop the system. Our database contains 33 letters and 10 digits (0 to 9). 120 speakers participated in the recording of the speech corpus: 80 adults (40 males and 40 females) and 40 children (20 boys and 20 girls). Among them, 60 are native speakers and the other 60 are speaking the language for the first time. After creating the database, we used Short-Time Energy (STE) to remove silence and compress the length of the input speech signals. Next, the features are extracted by a combination of two well-known approaches: the Mel-Frequency Cepstral Coefficients (MFCC) and the Discrete Wavelet Transform (DWT). The result is the Mel-Frequency Discrete Wavelet Coefficients (MFDWC) method. Finally, the classification step is conducted using Support Vector Machine (SVM) model. The experimental results prove that the system performs well by achieving an accuracy of 94.42% for the recognition of 10 digits, 90.82% for the recognition of 33 letters and 92.22% for the recognition of 33 letters and 10 digits.

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References

  1. Patil, U.G., Shirbahadurkar, S.D., Paithane, A.N.: Automatic speech recognition of isolated words in Hindi language using MFCC. In: Proceedings of the 2016 International Conference on Computing, Analytics and Security Trends (CAST), pp. 433–438. IEEE (2016). https://doi.org/10.1109/CAST.2016.7915008

  2. Satori, H., Harti, M., Chenfour, N.: Introduction to Arabic speech recognition using CMUSphinx system. arXiv Preprint (2007). arXiv:0704.2083

  3. Youcef, B.C., Elemine, Y.M., Islam, B., Farid, B.: Speech recognition system based on OLLO French Corpus by using MFCCs. In: Chadli, M., Bououden, S., Zelinka, I. (eds.) Recent Advances in Electrical Engineering and Control Applications. LNEE, vol. 411, pp. 326–331. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-48929-2_25

    Chapter  Google Scholar 

  4. Naithani, K., Thakkar, V.M., Semwal, A.: English language speech recognition using MFCC and HMM. In: Proceedings of the 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE), pp. 1–7. IEEE (2018). https://doi.org/10.1109/RICE.2018.8509046

  5. Nejme, F.Z., Boulaknadel, S., Aboutajdine, D.: Analyse automatique de la morphologie nominale Amazighe. In: Proceedings of the 2013 Actes de la conférence du Traitement Automatique du Langage Naturel (TALN), pp. 5–18 (2013)

    Google Scholar 

  6. Young, S.J., Young, S.: The HTK Hidden Markov Model Toolkit : Design and Philosophy. Cambridge University Engineering Department (1993)

    Google Scholar 

  7. Lamere, P., et al.: The CMU Sphinx-4 speech recognition system. In: Proceedings of the 2003 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2–5. IEEE (2003)

    Google Scholar 

  8. Ordowski, M., Deshmukh, N., Ganapathiraju, A., Hamaker, J., Picone, J.: A public domain speech-to-text system. In: Proceedings of the 1999 European Conference on Speech Communication and Technology. (1999)

    Google Scholar 

  9. Liu, X., Zhao, Y., Pi, X., Liang, L., Nefian, A.V.: Audio-visual continuous speech recognition using a coupled hidden Markov model. In: Proceedings of the 2002 International Conference on Spoken Language Processing (ICSLP) (2002)

    Google Scholar 

  10. El Ouahabi, S., Atounti, M., Bellouki, M.: Optimal parameters selected for automatic recognition of spoken Amazigh digits and letters using Hidden Markov Model Toolkit. Int. J. Speech Technol. 23(4), 861–871 (2020). https://doi.org/10.1007/s10772-020-09762-3

    Article  Google Scholar 

  11. Telmem, M., Ghanou, Y.: Amazigh speech recognition system based on CMUSphinx. In: Ben Ahmed, M., Boudhir, A.A. (eds.) SCAMS 2017. LNNS, vol. 37, pp. 397–410. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74500-8_37

    Chapter  Google Scholar 

  12. Satori, H., ElHaoussi, F.: Investigation Amazigh speech recognition using CMU tools. Int. J. Speech Technol. 17(3), 235–243 (2014). https://doi.org/10.1007/s10772-014-9223-y

    Article  Google Scholar 

  13. El Ouahabi, S., Atounti, M., Bellouki, M.: Toward an automatic speech recognition system for amazigh-tarifit language. Int. J. Speech Technol. 22(2), 421–432 (2019). https://doi.org/10.1007/s10772-019-09617-6

    Article  Google Scholar 

  14. Telmem, M., Ghanou, Y.: Estimation of the optimal HMM parameters for amazigh speech recognition system using CMU-Sphinx. Procedia Comput. Sci. 127, 92–101 (2018). https://doi.org/10.1016/j.procs.2018.01.102

    Article  Google Scholar 

  15. El Ouahabi, S., Atounti, M., Bellouki, M.: Amazigh isolated-word speech recognition system using hidden Markov model toolkit (HTK). In: Proceedings of the 2016 International Conference on Information Technology for Organizations Development (IT4OD), pp. 1–7. IEEE (2016). https://doi.org/10.1109/IT4OD.2016.7479305

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

    Google Scholar 

  17. Boulaknadel, S., Talha, M.: Analyse syntactico-sémantique de la langue amazighe (2013)

    Google Scholar 

  18. Ataa Allah, F., Boulaknadel, S.: Natural language processing for Amazigh language: challenges and future directions. In: Proceedings of the 2012 workshop on Language Technology for Normalisation of Less-Resourced Languages (SALTMIL8/AfLaT2012) (2012)

    Google Scholar 

  19. Ataa Allah, F., Boulaknadel, S.: Convertisseur pour la langue amazighe: script arabe-latin–tifinaghe. In: Proceedings of the 2011 Symposium International sur le Traitement Automatique de la Culture Amazighe, pp. 3–10 (2011)

    Google Scholar 

  20. Open Speech and Language Resources. https://www.openslr.org/resources.php. Accessed 20 Jan 2023

  21. Audacity software. https://www.audacityteam.org/. Accessed 17 Dec 2022

  22. Bachu, R.G., Kopparthi, S., Adapa, B., Barkana, B.D.: Separation of voiced and unvoiced using zero crossing rate and energy of the speech signal. In: Proceedings of the 2008 American Society for Engineering Education (ASEE), pp. 1–7 (2008)

    Google Scholar 

  23. Tufekci, Z., Gowdy, J.N.: Feature extraction using discrete wavelet transform for speech recognition. In: Proceedings of the 2000 IEEE SoutheastCon. ‘Preparing for The New Millennium’ (Cat. No. 00CH37105), pp. 116–123. IEEE (2000). https://doi.org/10.1109/secon.2000.845444

  24. Cutajar, M., Gatt, E., Grech, I., Casha, O., Micallef, J.: Comparative study of automatic speech recognition techniques. In: Proceedings of the 2013 IET Signal Processing, vol. 7, no. 1, pp. 25–46 (2013). https://doi.org/10.1049/iet-spr.2012.0151

  25. Hammami, N., Lawal, I.A., Bedda, M., Farah, N.: Recognition of Arabic speech sound error in children. Int. J. Speech Technol. 23(3), 705–711 (2020). https://doi.org/10.1007/s10772-020-09746-3

    Article  Google Scholar 

  26. Pandit, P., Makwana, P., Bhatt, S.: Automatic speech recognition of continuous speech signal of Gujarati language using machine learning. In: Sahni, M., Merigó, J.M., Jha, B.K., Verma, R. (eds.) Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy. AISC, vol. 1287, pp. 147–159. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9953-8_13

    Chapter  Google Scholar 

  27. Senthil Raja, G., Dandapat, S.: Speaker recognition under stressed condition. Int. J. Speech Technol. 13, 141–161 (2010). https://doi.org/10.1007/s10772-010-9075-z

    Article  Google Scholar 

  28. Padmanabhan, J., Johnson Premkumar, M.J.: Machine learning in automatic speech recognition: a survey. IETE Tech. Rev. 32(4), 240–251 (2015). https://doi.org/10.1080/02564602.2015.1010611

    Article  Google Scholar 

  29. Ghai, W., Singh, N.: Literature review on automatic speech recognition. Int. J. Comput. Appl. 41(8), 42–50 (2012). https://doi.org/10.5120/5565-7646

    Article  Google Scholar 

  30. Ali, H., Jianwei, A., Iqbal, K.: Automatic speech recognition of Urdu digits with optimal classification approach. Int. J. Comput. Appl. 118(9), 1–5 (2015). https://doi.org/10.5120/20770-3275

    Article  Google Scholar 

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Authors and Affiliations

  1. Research Team of Applied Mathematics and Intelligent Systems Engineering, National School of Applied Sciences, Ibn Zohr University, 80000, Agadir, Morocco

    Fadwa Abakarim & Abdenbi Abenaou

Authors
  1. Fadwa Abakarim
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  2. Abdenbi Abenaou
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Correspondence to Fadwa Abakarim .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Monash University, Clayton, VIC, Australia

    David Taniar

  4. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  5. University of Minho, Braga, Portugal

    Ana Cristina Braga

  6. University of Cagliari, Cagliari, Italy

    Chiara Garau

  7. National Technical University of Athens, Athens, Greece

    Anastasia Stratigea

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Abakarim, F., Abenaou, A. (2023). Enhancing Amazigh Speech Recognition System with MFDWC-SVM. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_31

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  • DOI: https://doi.org/10.1007/978-3-031-36805-9_31

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