基于深度学习的语音识别系统优化 -计算机科学与技术专业

摘要

本文深入探讨了基于深度学习的语音识别系统的优化路径，旨在克服当前技术在实际部署中遇到的多重挑战，从而推动语音识别技术的进一步发展与应用。随着人工智能技术的不断进步，语音识别技术作为人机交互的重要桥梁，其性能直接关系到用户体验的优劣。本文首先全面阐述了深度学习在语音识别领域的具体应用，包括实现高精度识别、促进语音翻译与合成技术的发展，以及提升复杂环境下的语音识别能力。然而，当前的基于深度学习的语音识别系统仍面临诸多难题，如噪声干扰导致的识别精度下降、数据标注成本高且质量参差不齐、模型训练过程中的过拟合问题，以及用户隐私与数据安全的保护需求。针对这些问题，本文系统性地提出了优化策略：通过引入深度学习噪声预处理技术和优化模型结构来增强系统的鲁棒性；利用半监督、无监督学习及自动标注工具提升数据标注效率与质量；借助数据增强、早期停止和迁移学习等技术优化模型训练过程；同时，采用差分隐私技术、实施严格的访问控制与权限管理，并建立安全审计与异常检测机制，以确保用户隐私与数据的安全。

关键词：深度学习；语音识别；噪声抑制；数据标注；模型优化

Abstract

This paper deeply discusses the optimization path of speech recognition system based on deep learning, aiming to overcome the multiple challenges encountered in the actual deployment of current technology, so as to promote the further development and application of speech recognition technology. With the continuous progress of artificial intelligence technology, speech recognition technology as an important bridge of human-computer interaction, its performance is directly related to the quality of user experience. This paper firstly describes the specific applications of deep learning in the field of speech recognition, including achieving high-precision recognition, promoting the development of speech translation and synthesis technology, and improving the speech recognition ability in complex environments. However, the current speech recognition system based on deep learning still faces many problems, such as the reduction of recognition accuracy caused by noise interference, high cost and uneven quality of data annotation, overfitting problems during model training, and the protection of user privacy and data security. To solve these problems, this paper systematically proposes optimization strategies: by introducing deep learning noise preprocessing technology and optimizing model structure to enhance the robustness of the system; Semi-supervised, unsupervised learning and automatic annotation tools are used to improve the efficiency and quality of data annotation. Optimize the model training process with data enhancement, early stop and transfer learning. At the same time, it adopts differential privacy technology, implements strict access control and permission management, and establishes security audit and anomaly detection mechanism to ensure user privacy and data security.

Key words: Deep learning; Speech recognition; Noise suppression; Data annotation; Model optimization

中文摘要 I

英文摘要 II

目录 III

引言 1

第1章、深度学习在语音识别中的具体应用 2

1.1、高精度语音识别 2

1.2、语音翻译 2

1.3、语音合成 2

1.4、复杂环境下的语音识别 3

第2章、当前基于深度学习的语音识别系统存在的问题 4

2.1、噪声抑制与鲁棒性问题 4