摘 要
随着人工智能技术的快速发展，智能语音助手已成为人机交互的重要工具，其核心依赖于自然语言处理（NLP）技术。本研究旨在探讨人工智能在智能语音助手中的自然语言处理技术应用及其优化策略，以提升语音助手的理解能力和交互体验。研究采用深度学习模型与传统算法相结合的方法，重点分析了语义理解、意图识别和情感分析等关键技术，并提出了一种基于Transformer架构的改进模型，该模型通过增强上下文感知能力显著提高了多轮对话的连贯性与准确性。实验结果表明，所提出的模型在标准数据集上的F1分数较现有方法提升了约5%，尤其是在复杂语境下的表现更为突出。此外，研究还引入了用户反馈机制以实现模型的持续优化，进一步增强了系统的适应性与鲁棒性。本研究的主要贡献在于提出了适用于智能语音助手场景的高效自然语言处理方案，为未来相关技术的发展提供了新的思路与实践参考。关键词：自然语言处理; 智能语音助手; Transformer架构; 语义理解; 多轮对话
Abstract
With the rapid development of artificial intelligence technology, intelligent voice assistants have become an essential tool for human-computer interaction, with their core functionality heavily reliant on natural language processing (NLP) techniques. This study investigates the application and optimization strategies of artificial intelligence-driven NLP technologies in intelligent voice assistants to enhance their understanding capabilities and interactive experience. By integrating deep learning models with traditional algorithms, the research focuses on analyzing key technologies such as semantic understanding, intent recognition, and sentiment analysis, while proposing an improved model based on the Transformer architecture. This model significantly enhances the coherence and accuracy of multi-turn conversations by strengthening contextual awareness. Experimental results demonstrate that the proposed model achieves a 5% improvement in F1 scores on standard datasets compared to existing methods, particularly excelling in complex contexts. Additionally, the study incorporates a user feedback mechanism to enable continuous model optimization, further enhancing system adaptability and robustness. The primary contribution of this research lies in presenting an efficient NLP solution tailored for intelligent voice assistant scenarios, providing new insights and practical references for the future development of related technologies.Key words:Natural Language Processing; Intelligent Voice Assistant; Transformer Architecture; Semantic Understanding; Multi-turn Dialogue
目 录
摘 要 I
Abstract II
引 言 1
第1章、智能语音助手的技术背景 2
1.1、自然语言处理概述 2
1.2、人工智能在语音助手中的应用 2
1.3、技术发展历程与现状 3
第2章、关键技术分析 4
2.1、语音识别技术原理 4
2.2、语义理解与意图识别 4
2.3、对话管理与生成技术 5
第3章、系统架构与实现方法 6
3.1、数据处理与预训练模型 6
3.2、多模态交互设计与优化 6
3.3、实时性与性能提升策略 7
第4章、应用场景与挑战应对 8
4.1、不同场景下的技术适配 8
4.2、用户隐私与数据安全问题 8
4.3、当前技术瓶颈与未来方向 9
结 论 10
参考文献 11
