Automatic Speech Recognition of Smart Home Based on Bat-Pelican Optimization Algorithm

Abstract One of the principal obstacles in Ambient Assisted Living (AAL) is to create smart health houses that can predict the requirements of their occupants while ensuring their safety and comfort. Therefore, it is crucial to enhance the user experience with smart home devices by implementing systems that instinctively respond to voice commands using microphones instead of touch-based interfaces. This trend is characterized by the rapid expansion of interconnected devices. In diverse situations, such as smart homes, smart buildings, and smart cities, networked electronic devices are gaining popularity. This chapter intends to improve the accuracy of speech recognition in home automation systems by realizing speech recognition by using the Bat-Pelican Optimization Algorithm (BPOA). The idea of this model is to examine the concept of BPOA that assists in the construction of systematic simulations with the aid of computers. By bringing a rough calculation solution that develops the accuracy of this model’s findings, BPOA’s execution shows an improvement in the feature selection process in speech recognition. Utilizing the BPOA technique enhances the accuracy of the speech recognition system and integrates the Adaptive Neuro-Fuzzy Inference (ANFI) for the classification component. The results of the chapter confirmed the effectiveness of the methods used.