Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Toward an automatic speech recognition system for amazigh-tarifit language

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

Abstract

This work aims at contributing to the Amazigh language Automatic Speech Recognition (ASR). We have studied and realized an automatic speech recognition system, using an environment totally based on the Amazigh-Tarifit language. In this framework, we have first constructed a new Amazigh-Tarifit speech corpus, which was used to assess and make the results of this work undergo a test. In fact, this paper has two objectives: The first one is to collect a Medium-Vocabulary Isolated Word speech corpus, which will serve as a corpus for the Amazigh speech researchers. The second target is to develop an Amazigh ASR system using this speech corpus (187 distinct isolated-words). The speech corpus was recorded by 50 individuals (25 males and 25 females) Amazigh-Tarifit native speakers. The system was evaluated on a speaker-independent approach. The tests were carried out basing essentially on two parameters: the Gaussian Mixture Distributions (GMM), and tied states (senones). The Word Error Rate (WER) achieved 8, 20%.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abu Shariah, M. A. M., Ainon, R. N., Zainuddin, R., Khalifa, O. O. (2007). Human computer interaction using isolated-words speech recognition technology, 2007 International Conference on Intelligent and Advanced Systems, Kuala Lumpur, (pp. 1173–1178).

  • Abushariah, M. A. M., Ainon, R. N., Zainuddin, R., Alqudah, A. A. M., Elshafei, M. A., & Khalifa, O. O. (2011). Modern standard Arabic speech corpus for implementing and evaluating automatic continuous speech recognition systems. Journal of the Franklin Institute, 349, 2215–2242.

    Article  Google Scholar 

  • Abushariah, M. A. M., Ainon, R. N., Zainuddin, R., Khalifa, O. O., & Elshafei, M. (2010). Phonetically rich and balanced arabic speech corpus: An overview. In Proceedings of International Conference on Computer and Communication Engineering (ICCCE’10), Kuala Lumpur, (pp. 1–6).

  • Al-Sulaiti, L., & Atwell, E. (2006). The design of a corpus of Contemporary Arabic. International Journal of Corpus Linguistics, 11, 135–171. https://doi.org/10.1075/ijcl.11.2.02als.

    Article  Google Scholar 

  • Ameur, M., Bouhjar, A., Boukhris, F., Boukouss, A., Boumalk, A., Elmedlaoui, M., et al. (2004). Initiation à la langue Amazighe. CAL, Rabat: Publications de l’IRCAM.

    Google Scholar 

  • Ananthi, S., & Dhanalakshm, P. (2013). Speech Recognition system and isolated word recognition based on hidden markov model (HMM) for hearing impaired. International Journal of Computer Applications, 73(20), 30–34.

    Article  Google Scholar 

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

  • Boukhris, F., Boumalk, A., El Moujahid, E., & Souifi, H. (2008). “La Nouvelle Grammaire de l’Amazighe”, IRCAM, CAL. Rabat: Publications de l’IRCAM.

    Google Scholar 

  • Boukous, A. (1995). Société, langues et cultures au Maroc: Enjeux symboliques. Casablanca, Maroc: Najah El Jadida.

    Google Scholar 

  • Boukous, A. (2009). Phonologie de l’Amazigh. Rabat: Institut royal de la culture Amazigh.

    Google Scholar 

  • Clarkson, P., & Rosenfeld, R. (1997). Statistical language modeling using the CMU-Cambridge toolkit, In Proceedings of the 5th European Conference on Speech Communication and Technology (pp. 2707–2710), Rhodes, Greece.

  • CMU Sphinx Open Source Speech Recognition Engines, Retrieved February 10, 2013, from http://www.cmusphinx.sourceforge.net/html/cmusphinx.php.

  • El Amrani, M. Y., Rahman, M. M. H., Wahiddin, M. R., & Shah, A. (2016). Building CMU Sphinx language model for the Holy Quran using simplified Arabic phonemes. Egyptian Informatics Journal, 17, 305–314.

    Article  Google Scholar 

  • el Ghazi, A., Daoudi, C., & Idrissi, N. (2014). Automatic speech recognition for tamazight enchained digits. World Journal Control Science and Engineering, 2(1), 1–5. https://doi.org/10.12691/wjcse-2-1-1.

    Google Scholar 

  • Elouahabi, S., Atounti, M., & Bellouki, M. (2016a). Amazigh Isolated-Word speech recognition system using Hidden Markov Model toolkit (HTK), 2016 International Conference on Information Technology for Organizations Development (IT4OD), (pp. 1–7).

  • Elouahabi, S., Atounti, M.. & Bellouki, M. (2016b). Building HMM Independent Isolated Speech Recognizer System for Amazigh Language, Europe and MENA Cooperation Advances in Information and Communication Technologies, 2016, pp. Europe and MENA Cooperation Advances in Information and Communication Technologies Volume 520 of the series Advances in Intelligent Systems and Computing, (pp. 299–307).

  • Es Saady, Y., Ait Ouguengay, Y., Rachidi, A., El Yassa, M., & Mammass, D. (2009). Adaptation d’un correcteur orthographique existant à la langue Amazighe: cas du correcteur Hunspell, Actes du 1er symposium international sur le traitement automatique de la culture amazighe, (pp. 149–158).

  • European Language Resources Association (ELRA). http://www.elra.info/.

  • Fakir, M., Bouikhalene, B., & Moro, K. (2009) Skeletonization methods evaluation for the recognition of printed tifinaghe characters, Actes du 1er symposium international sur le le traitement automatique de la culture amazighe, (pp. 33–47).

  • Ghai, W., & Singh, N. (2012). Analysis of automatic speech recognition systems for indo-aryan languages: Punjabi A Case Study. International Journal of Soft Computing and Engineering (IJSCE), 2(1), 379–385.

    Google Scholar 

  • EL Ghazi, A., Daoudi, C., & Idrissi, N. (2012). Automatic speech recognition system concerning the moroccan dialect (Darija and Tamazight). International Journal of Engineering Science and Technology (IJEST), 4(03), 1–5.

    Google Scholar 

  • Greenberg, J. H. (1966) The languages of Africa. Mouton: The Hague. Linguistic Data Consortium (LDC), various corpus resources on http://www.ldc.upenn.edu.

  • Hoge, H., Tropf, H. S., Winski, R., Heuvel, H. V. D., Haeb-Umbach, R., & Choukri, K. (1997) European speech databases for telephone applications. 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, Munich, (vol. 3, pp. 1771–1774).

  • https://www.ethnologue.com/statistics.

  • Hyassat, H., & Abu-Zitar, R. (2006). Arabic speech recognition using SPHINX engine. International Journal of Speech Technology, 9, 133. https://doi.org/10.1007/s10772-008-9009-1.

    Article  Google Scholar 

  • Kimutai, S. K., Milgo, E., & Gichoya, D. (2013). Isolated swahili words recognition using sphinx4. International Journal of Emerging Science and Engineering (IJESE), 2(2), 2319–6378.

    Google Scholar 

  • Kumar, K., Jain, A., & Aggarwal, R. K. (2012). A Hindi speech recognition system for connected words using HTK. International Journal of Computational Systems Engineering, 1(1), 25–32.

    Article  Google Scholar 

  • Lamere, P., Kwok, P., Walker, W., Gouvêa, E. B., Singh, R., Raj, B., et al. (2003). Design of the CMU Sphinx-4 decoder, In Proceedings of the 8th European conference on speech communication and technology (pp. 1181–1184), Geneve, Switzerland.

  • Outahajala, M., Zenkouar, L., & Rosso, P. (2011). Building an annotated corpus for Amazigh, In Proceedings of 4th international conference on Amazigh and ICT, Rabat, Morocco.

  • Pokhariya, J. S., & Mathur, S. (2014). Sanskrit speech recognition using hidden markov model toolkit. International Journal of Engineering Research & Technology (IJERT), 3(10), 93–98.

    Google Scholar 

  • Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286.

    Article  Google Scholar 

  • Rachidi, A., & Mammass, D. (2007) Vers un système de traduction automatique en ligne des documents amazighs fondé sur les graphes UNL, Revue E-TI, (vol. 4).

  • Robinson, T., Fransen, J., Pye, D., Foote, J., & Renals, S. (1995). WSJCAMO: a British English speech corpus for large vocabulary continuous speech recognition, 1995 International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, USA, (vol. 1, pp. 81–84).

  • Rudzicz, F. (2007). Comparing speaker-dependent and speaker-adaptive acoustic models for recognizing dysarthric speech, In Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS ‘07) (pp. 255–256), New York: ACM.

  • Sameti, H., Veisi, H., Bahrani, M., Babaali, B., Hosseinzadeh, K., et al. (2011). A large vocabulary continuous speech recognition system for Persian language. EURASIP Journal on Audio, Speech, and Music Processing, 2011, 6.

    Article  Google Scholar 

  • Satori, H., & EL Haoussi, F. (2014). Investigation Amazigh speech recognition using CMU tools. International Journal of Speech Technology, 17(3), 235–243.

    Article  Google Scholar 

  • Speech Recognition, http://en.wikipedia.org/wiki/Speech_recognition.

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

    Article  Google Scholar 

  • Vrinda, C., & Shekhar, C. (2013). Speech recognition system for English language. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 919–922.

    Google Scholar 

Download references

Acknowledgements

We would like to extend our appreciation to the students of the faculty Polydisciplinary of Nador, Morocco, Department of Mathematics and computer sciences, who have, help us to collect the Amazigh speech corpus within the framework of the graduate programs of the faculty Polydisciplinary of Nador, Morocco, in the period of 2015-2016.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Atounti.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-019-09617-6

Keywords

Navigation