Automatic Speech Recognition bay System MFCC-LPC-SVM for energy optimization in intelligent building

Abstract

the problem of smart buildings is their overconsumption of energy; in this article we propose a solution to this problem. This solution is to give optimum comfort to the people who occupy the smart buildings, but sometimes these people are not satisfied with this comfort, so we realized a system of automatic speech recognition, to give the possibility for these people to change the parameters of comfort, and to obtain the desired comfort. This solution allows to optimize the smart building energy, for example if a room is empty, then the person who has been in this room must say off when leaving this room, in this case the automatic recognition system of speech, instructs the system that managed the energy to turn off the lamps and lower the heater or air conditioner. This automatic speech recognition system using the MFCC method (Mel frequency cepstral coefficient) and a LPC method (linear prediction coding) for the representation of the speech signal, and SVM (support vector machine), for speech recognition. The recognition will be of global type, the speech signal will be parameterized using the MFCC coefficients and LPC coefficients to form the input vector of the recognition system, the SVM will be used for the learning phase and recognition phase. An experiment will be implemented using an input corpus, the vocabulary used in this corpus consists of 10 words in English language. The results obtained will be analyzed and compared with other similar works that use other techniques of automatic speech recognition.