RECONOCIMIENTO DE COMANDOS POR VOZ CON MÁQUINAS DE SOPORTE VECTORIAL A TRAVÉS DE BANDAS ESPECTRALES Voice commands recognizing using support vector machines and spectral bands

In this paper a voice command recognizing methodology using Support Vector Machines (SVM) is proposed. This is an important task in autonomous and semi-autonomous systems, because it is a natural and useful interaction way, especially in situations where there are special limitations as low visibility, low or any possibility of physical contact, among others. As application example, voice registered signals are characterized by using spectral bands and next these are classified by using SVMs. The proposed methodology is tested in vowels identification, having obtained a 98% of average successful results

[1]  Luis Gerardo Guerrero-Ojeda,et al.  A voice recognition system for speech impaired people , 2004, 14th International Conference on Electronics, Communications and Computers, 2004. CONIELECOMP 2004..

[2]  J. José.,et al.  Localización de faltas en sistemas de distribución de energía eléctrica usando métodos basados en el modelo y métodos basados en el conocimiento , 2006 .

[3]  M. Bodruzzaman,et al.  Parametric feature-based voice recognition system using artificial neural network , 1993, Proceedings of Southeastcon '93.

[4]  Gamini Dissanayake,et al.  New wavelet-based pitch detection method for human-robot voice interface , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[5]  G. Pacnik,et al.  Voice operated intelligent wheelchair - VOIC , 2005, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[6]  N. Botros,et al.  Automatic voice recognition using artificial neural network approach , 1989, Proceedings of the 32nd Midwest Symposium on Circuits and Systems,.

[7]  S. Kumar,et al.  EMG based voice recognition , 2004, Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004..

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  A. E. Barbour,et al.  A new algorithm for pattern recognition of voices , 1992, [1992] Proceedings of the 35th Midwest Symposium on Circuits and Systems.

[10]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .