Personalized Power Consumption Control Method for Intelligent Buildings That Perceiving Speech Recognition Identity

Abstract

Abstract With the development of IoT technology, the meaning of the concept of energy conservation and environmental protection in people’s minds is also constantly changing, and it is moving towards the direction of intelligence and personalization. At present, China’s economy has entered the era of artificial intelligence. Electricity control such as building lighting has gradually changed from a single manual control to a multi-modal control. This paper designs an embedded control method based on speech recognition of personnel to achieve personalized power consumption, and improves the existing defects of intelligent power consumption in buildings such as unconfirmed operators and single decision-making factors. The method is to extract the mel frequency cepstrum coefficient feature vector after preprocessing the speech signal; Aiming at the problem of singularity matrix in the parameter estimation of Gaussian mixture model, the modified EM algorithm was used to optimize the parameter estimation of the Gaussian mixture model to complete the identification of personnel. The experimental test results show that the system can control the electricity facilities in combination with the environmental information and realize the personalized lighting control function while confirming the identity of the personnel.