Navigation security module with real-time voice command recognition system

• Autorzy: Yagimli M. , Tezer H.K.
• Języki publikacji: EN

Abstrakty

EN

The real-time voice command recognition system used for this study, aims to increase the situational awareness, therefore the safety of navigation, related especially to the close manoeuvres of warships, and the courses of commercial vessels in narrow waters. The developed system, the safety of navigation that has become especially important in precision manoeuvres, has become controllable with voice command recognition-based software. The system was observed to work with 90.6% accuracy using Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) parameters and with 85.5% accuracy using Linear Predictive Coding (LPC) and DTW parameters

• Wydawca: -
• Czasopismo: Polish Maritime Research
• Rocznik: 2017
• Tom: 24
• Numer: 2
• Opis fizyczny: p.17-26,fig.,ref.

Twórcy

autor

Yagimli M.

• Department of Property Protection and Security, Okan University, 34722, Kadikoy, Istanbul ,Turkey

autor

Tezer H.K.

• Institute of Naval Science and Engineering, Turkish Navy Academy, Tuzla, Turkey

Bibliografia

• 1. Lazarowska A.: Decision support system for collision avoidance at sea. Polish Maritime Research, 2012 (Special Issue), pp.19-24.
• 2. Lazarowska A.: Swarm intelligence approach to safe ship control. Polish Maritime Research, 2015(4), pp. 33-40.
• 3. Zhizeng L., Jinghing Z.: Speech recognition and its applica-tion in voiced-based robot control system. International Conference on Intelligent Mechatronics and Automation, 0-7803-8748-1, 2004.
• 4. Bala A., Kumar A., Birla N.: Voice Command Recognition System Based on MFCC and DTW. International Journal of Engineering Science and Technology Vol. 2 (12), 7335-7342, 2010.
• 5. Vashisht D., Sharma S., Dogra L.: Design of MFCC and DTW for Robust Speaker Recognition. International Journal of Electrical&Electronics Engineering Vol 2 (3), 1694-2426, 2015.
• 6. Ferrando F., Nouveau G., Philip B., Pradeilles P., Soulenq V., Van-Staen G., Courmontagne P.: A Voice Recognition System for a Submarine Piloting. 1-424 4-2523-5/09- IEEE , 2009.
• 7. Smith S .W. : The Scientist’s and Engineer’s Guide to Digital Signal Processing. California Technical Publishing, ISBN 0-96-601764-1, 1999.
• 8. Yagimli M., Akar F.: Digital Electronics. Beta, ISBN: 9786053777526, Istanbul 2014.
• 9. Proakis J. & Manolakis D.: Digital Signal Processing Principles, Algorithms and Applications (3rd edition). New Jersey : Prentice-Hall Inc., 1996.
• 10. Karakas M.: Computer Based System Control Using Voice Input. M.Sc. thesis, Dokuz Eylul University, 2010.
• 11. Demirci M.: Computer Aided Voice Recognition System Design, M.Sc. thesis, Istanbul University, 2005.
• 12. Lindasa lwa M, Mu mtaj B, Ela mva zut hi I.: Vo i c e R e c o g n i -tion Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques. Journal of Computing, Volume 2, Issue 3, pp.138-143, ISSN 2151-9617, March 2010.
• 13. Rabiner L. R., Shafer R. W.:Digital Processing of Speech Signals. Prentice Hall Inc., September 1978.
• 14. Huang X., Acero A, Hon H.W.: Spoken Language Process-ing: A Guide to Theory, Algorithm and System Develop-ment (1st Ed.). Prentice Hall PTR, ISBN 0-13-022616-5, 2001.
• 15. Lipeika A., Lipeikien J., Telksnys L.:Development of Iso-lated Word Speech Recognition System. INFORMATICA, Vol. 13, No. 1, 37–46 Institute of Mathematics and Infor-matics, 2002.
• 16. Juang B.H., Wang D.Y., Gray A.H.: Distortion performance of vector quantization for LPC voice coding. IEEE Tranc. on Acoustic Speech and Signal Processing, ASSP-30 (2), 294–304.
• 17. Jiang Z., Huang H., Yang S., Lu S., Hao Z.: Acoustic Fea-ture Comparison of MFCC and CZT-based Cepstrum for Speech Recognition. IEEE 2009 Fifth International Conference on Natural Computation, 978-0-7695-3736-8/09, 2009.
• 18. Price J., Eydgahi A.: Design of Matlab®-Based Automatic Speaker Recognition Systems. 9th International Confer-ence on Engineering Education-T4J-1, July 2006.
• 19. Phoophuangpairoj R.: Using Multiple HMM Recognizers and the Maximum Accuracy Method to Improve Voice-Controlled Robots. 2011 International Symposium on Intelligent Signal Processing and Communication Sys-tems (ISPACS) December 7-9, 2011.
• 20. Petrushin V.A.: Emotion in Speech Recognition and Application to Call Centres. Andersen Consulting, 3773 Willow Rd., 2009.

Identyfikatory

DOI

10.1515/pomr-2017-0046