Building a Smart Interactive Kiosk for Tourist Assistance

Authors

  • Hanane Amessafi Laboratory of electrical engineering and energetic systems Faculty of Sciences,Kenitra, Ibn Tofail University, Kenitra, Morocco
  • Reda Jourani Information Systems Engineering Group Polydisciplinary Faculty of Tétouan, AbdelMalek Essaâdi University, Tétouan, Morocco
  • Adil Echchelh Laboratory of electrical engineering and energetic systems Faculty of Sciences,Kenitra, Ibn Tofail University, Kenitra, Morocco
  • Houssain Oulad Yakhlef Modelling and information theory Group Polydisciplinary Faculty of Tétouan, AbdelMalek Essaâdi University, Tétouan, Morocco

DOI:

https://doi.org/10.14738/tmlai.54.3326

Keywords:

Automatic Language Recognition, Automatic Speech Recognition, keyword extraction, question answering, Gaussian Mixture models, tourism

Abstract

The tourism sector in morocco is increasing rapidly, is well developed, with a strong tourist industry focused on the country's coast, culture, and history. Morocco is the most politically stable countrie in North Africa, which has allowed tourism to develop. There are some tourist attractions spread over a wide geographical area, which are only visited by a few people at specific times of the year. Additionally, having human tourist guides everywhere and speaking different languages is unfeasible. This work deals with the build of a smart interactive kiosk for tourist assistance. In order to do this, it is necessary to start with an Automatic Language Recognition to recognize the language used by the tourist, then extracting keywords from what the tourist said by using Automatic Speech Recognition techniques among others, and finally the system analyses and ansers to the tourist query by suggesting a set of related informations.

 

 

References

(1) Garrido, P., Barrachina, J., Martinez, F., Seron, F., 2017. Smart tourist information points by combining agents, semantics and AI techniques. Comput. Sci. Inf. Syst. 14, 1–23. doi:10.2298/CSIS150410029G

(2) Towards a cognitive approach to human machine cooperation in dynamic situations : JEAN-MICHEL HOC

(3) DePaulo, J. Lindsay, B. Malone, L. Muhlenbruck, K. Charlton, and H. Cooper. 2003. Cues to deception. Psychological Bulletin, 129(1):74—118.

(4) Computer Simulation of Human Thinking : Allen Newell, Herbert A.Simon

(5) The Prospects for Psychological Science in Human-Computer Interaction : Allen Newell, Stuart K. Card

(6) Youcef B.C., Elemine Y.M., Islam B., Farid B. (2017) 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. Lecture Notes in Electrical Engineering, vol 411. Springer, Cham

(7) Patel, K., Prasad, R.K.: Speech recognition and verification using MFCC & VQ. Int. J. Emerg. Sci. Eng. (IJESE) 1(7) (2013). ISSN: 2319–6378

(8) Jourani, R. and Daoudi, K. and Andre-Obrecht, R. and ´Aboutajdine, D., “Large Margin Gaussian mixture models for speaker identification,“ in Proc. of Interspeech, 2010, pp. 1441–1444

(9) Achraf Ben Romdhane, Salma Jamoussi, Abdelmajid Ben Hamadou, Kamel Smaili Phrase-Based Language Model in Statistical Machine Translation ;International Journal of Computational Linguistics and Applications, Alexander Gelbukh,

Journal articles

(10) Jourani, R., Daoudi, K., Andr´e-Obrecht, R., and Aboutajdine, D. (Online First). Discriminative speaker recognition using Large Margin GMM. Journal of Neural Computing &Applications, doi :10.1007/s00521-012-1079-y.

(11) Jourani, R., Daoudi, K., Andr´e-Obrecht, R., and Aboutajdine, D. (2011). Speaker verification using Large Margin GMM discriminative training. In Proc. of ICMCS, page 1–5

(12) M.E. Al-Ahdal and N.M. Tahir, “Review in sign language recognition systems,” in ISCI’12 : IEEE Symposium

(13) A.K. Sahoo, G. S. Mishra, and K. K. Ravulakollu,“Sign language recognition : State of the art,” ARPN Journal of Engineering and Applied Sciences, vol. 9,no. 2, pp. 116–134, 2014

(14) M. Koppel, S. Argamon, and A. Shimoni. 2002. Automatically categorizing written texts by author gender. Literary and Linguistic Computing, 4(17):401– 412.

(15) Karn ("Design And Evaluation Of A Phonological Phrase Parser For Spanish Text-To-Speech", Fourth International Conference on Spoken Language, Oct. 1996).*

(16) T.M. Derwing, M.J. Munro, and M. Carbonaro, “Does popular speech recognition software work with ESL speech?”, TESOL Quarterly 34, 592-603, 2000. [2] D. Coniam, “Voice recognition software accuracy

(17) T. Chen, C. Huang, E. Chang, and J. Wang, “Automatic accent identification using gaussian mixture models,” in Automatic Speech Recognition and Understanding, 2001. ASRU’01. IEEE Workshop on. IEEE, 2001, pp. 343–346

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Published

2017-09-01

How to Cite

Amessafi, H., Jourani, R., Echchelh, A., & Yakhlef, H. O. (2017). Building a Smart Interactive Kiosk for Tourist Assistance. Transactions on Engineering and Computing Sciences, 5(4). https://doi.org/10.14738/tmlai.54.3326

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems