Beyond Textual Analysis: Framework for CSAT Score Prediction with Speech and Text Emotion Features

In today’s competitive landscape, accurately assessing customer satisfaction is crucial for business success. This paper presents an innovative methodology for call centers, leveraging advanced speaker diarization, sentiment analysis, and speech emotion recognition (SER) to gain a nuanced understanding of customer needs and preferences. By integrating these techniques, our approach predicts customer satisfaction scores (CSAT), empowering businesses to refine products, optimize services, and make data-driven decisions. Experimental validation demonstrates the effectiveness of our methodology in uncovering actionable intelligence from call center interactions, enabling businesses to enhance customer experience and drive continuous improvement.