This paper presents the use of a Multi-Layer Perceptron Neural Nets (MLP-NN) for voice recognition dedicated to generating robot commands. Our main goal concerns the estimation of the minimal number of elements required for the learning process in order to ensure an acceptable success of the neural nets recognition. As the MLP requires references for the spoken words, we have provided these references by means of a supervised classifier based on the mean square error.
An experimental approach has been followed for the design of experiments enabling to determine the minimal elements in the sample for each voice command. Satisfactory results have been obtained leading to a better understanding of variability of the system functioning. Finally, we have noticed that the success rate of the MLP and the minimal number of elements used for the learning process depend on the spoken word structure and of the variability of the actual work situation (word length, noise, speaker, etc).
