Research on AI-Based Multilingual Natural Language Processing Technology and Intelligent Voice Interaction System

Abstract

It has shown broad development prospects in intelligent application fields such as cross-lingual voice interaction, machine translation, and voice assistants. Faced with challenges such as complex speech features, diverse semantic structures, and limited terminal deployment, technical systems need to achieve effective collaboration between recognition accuracy, semantic consistency, and operational efficiency. The application of training language models, context-aware mechanisms, and end-cloud collaborative structures provides a new path for optimizing system performance. This article focuses on key aspects such as speech recognition, semantic understanding, and deployment mechanisms, exploring technical bottlenecks and feasible improvement solutions in multilingual environments, with the aim of providing a theoretical basis and practical guidance for cross-language applications of intelligent speech systems.