Intelligent manufacturing systems - automatic speech recognition system

• Autorzy: Gregor M. , Michulek T
• Języki publikacji: EN

Abstrakty

EN

The University of Žilina and the Central European Institute of Competitiveness have been conducting research in development of Intelligent Manufacturing System. One of areas researched was Automatic Speech Recognition (ASR) and control of manufacturing processes through voice control. This paper presents chosen results from research done at the University of Zilina and at the Central European Institute of Technology.

Słowa kluczowe

EN

manufacturing processes; IMS; ASR

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2009
• Tom: Vol. 5, no 1
• Strony: 55--68
• Opis fizyczny: Bibliogr. 11 poz., fig.

Twórcy

autor

Gregor M.

• University of Žilina, Institute of Competitiveness and Innovation, Univerzitná 1, 010 26 Žilina, Slovak Republic

autor

Michulek T

• Central European Institute of Technology, Univerzitná 8413/6, 010 08 Žilina, Slovak Republic

Bibliografia

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• [2] TEBELSKIS J.: Speech Recognition using Neural Networks. Carnegie Mellon University, Thesis 1995.
• [3] TRENTIN E., GORI M.: A survey of hybrid ANN/HMM models for automatic speech recognition. In: Neurocomputing, 37(1/4), 2001.
• [4] VAN DER SMAGT P., KRÖSE B.: An Introduction to Neural Networks. 1996. http://www.avaye.com/files/articles/nnintro/nn_intro.pdf
• [5] TAN C. L., JANTAN A.: Digit Recognition Using Neural Networks. In: Malaysian Journal of Computer Science, Vol. 17 No. 2, 2004.
• [6] GEMELLO R., MANA F., ALBESANO D.: Hybrid HMM/Neural Network basedSpeech Recognition in Loquendo ASR. http://www.loquendo.com/en/brochure/Speech_Recognition_ASR.pdf
• [7] HOSOM J. P., COLE R., FANTY M.: Speech Recognition Using Neural Networks. http://www.cslu.ogi.edu/tutordemos/nnet_recog/recog.html
• [8] IVANECKÝ J.: Automatická transkripcia a segmentácia reči. Technická univerzita v Košiciach, Fakulta elektrotechniky a informatiky, Dizertačná práca 2003.
• [9] GALES M. J. F.: Model-based Techniques for Noise Robust Speech Recognition. Gonville and Caius College, University of Cambridge, Dissertation 1995. svr-www.eng.cam.ac.uk/~mjfg/thesis.pdf
• [10] MORRIS A. C., HAGEN A., BOURLARD H.: The Full Combination Sub-Bands Approach To Noise Robust HMM/ANN-based ASR. In: Proc. Eur. Conf. Speech Commun. Technol., pp. 599-602, 1999.
• [11] OKAWA S., BOCCHIERI E., POTAMIANOS A.: Multi-band Speech Recognition in Noisy Environments. In: Proc. IEEE Intl. Conf. Acoust., Speech, SignalProcessing, pp. 641–644, 1998.