Developing an adaptive model for customer service conversations: leveraging affective anthropomorphic intelligent systems to enhance customer emotion and trust

Abstract

In the field of customer service, effectively managing customer emotions is crucial for building trust and enhancing customer satisfaction. This paper presents an adap- tive model leveraging affective anthropomorphic intelligent systems to detect and respond to customer emotions in real-time, thereby improving the quality of customer service interactions. Our approach integrates several AI models: a speech-to-text (STT) model to transcribe customer speech, an emotion detection model to analyze emotional states, and a large language model to generate contextually appropriate responses. Additionally, we introduce a novel AI model capable of detecting emotions based on the tone and intensity of the customer’s voice, significantly enhancing the system’s ability to interpret emotional nuances. The proposed system is designed to emulate human-like empathy and adaptability, addressing customer queries with sensitivity to their emotional state. Through rigorous testing and evaluation, our model demonstrates superior performance in emotion detection and response generation, highlighting its potential to transform customer service by fostering greater customer trust and satisfaction"