RLHF-Enhanced Multilingual Audio Content Understanding: A Cross-Cultural Sentiment Analysis Framework for Global Communication

Abstract

The proliferation of multilingual audio content across global communication platforms presents significant challenges in understanding cross-cultural sentiment expressions. This paper introduces a novel framework that integrates Reinforcement Learning from Human Feedback (RLHF) with advanced multilingual audio processing techniques to enhance cross-cultural sentiment analysis capabilities. Our approach addresses the complexities of language-specific emotional expressions and cultural nuances through an adaptive learning mechanism that continuously refines understanding based on human feedback. The proposed framework demonstrates superior performance in identifying sentiment patterns across diverse linguistic and cultural contexts, achieving accuracy improvements of 18.3% over traditional approaches. The system incorporates multi-dimensional feedback fusion mechanisms and dynamic reward estimation to optimize sentiment classification across 12 major languages. Experimental results reveal enhanced cross-cultural communication effectiveness through improved sentiment detection accuracy and cultural context preservation. The framework's applications extend to global diplomatic communications, international business negotiations, and cross-border social media monitoring, contributing to more effective intercultural understanding and communication facilitation in increasingly connected world environments.