Abstract
SummarySmart “discrete” technologies are taking over everyday activities with an intended shift towards complete automation. With this, ambient intelligence (AmI) has come under the limelight, to make these routines subtle and automated, enabled through the widespread adoption of the ubiquitous and pervasive Internet of Things (IoT). AmI is context‐driven depending largely on user input through various interfaces, which is nothing but the interplay between IoT and artificial intelligence (AI). Despite extensive incorporation of personal digital assistants and smart home systems, AI is not yet capable of accurately interpreting and responding to human emotions. This paper aims to present one such application to gauge the emotional and cognitive state of the user and respond appropriately, be it ambient lighting or an emergency SOS. The proposed model extracts network packets to work with audio messages on the real‐time transport protocol (RTP) layer and presents a lightweight multilayer perceptron (MLP)‐based speech emotion recognition (SER) model for emotional analysis with an accuracy of 80.52%. It also features a simple confidence rating system for further optimization.
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