摘 要
随着人工智能技术的快速发展，语音识别作为人机交互的重要手段，在多个领域展现出巨大的应用潜力。然而，传统语音识别方法在处理复杂场景时面临诸多挑战，如鲁棒性不足、模型泛化能力有限等问题。为解决这些问题，本研究聚焦深度学习技术在语音识别中的关键应用，旨在通过改进模型架构和优化算法提升语音识别系统的性能。研究采用卷积神经网络（CNN）、循环神经网络（RNN）以及注意力机制等先进技术，构建了多层次的端到端语音识别框架，并结合大规模数据集进行训练与验证。实验结果表明，所提出的模型在噪声环境下的识别准确率较传统方法提升了15%以上，同时显著降低了错误率。
关键词：语音识别 深度学习 卷积神经网络
Abstract
With the rapid development of artificial intelligence technology, speech recognition, as an important means of human-computer interaction, has shown great application potential in many fields. However, traditional speech recognition methods face many challenges when dealing with complex scenarios, such as insufficient robustness and limited model generalization ability. To solve these problems, this study focuses on the key application of deep learning technology in speech recognition, aiming to improve the performance of speech recognition system by improving the model architecture and optimizing the algorithm. Advanced technologies such as convolutional neural network (CNN), recurrent neural network (RNN) and attention mechanism are used to build a multi-level end-to-end speech recognition fr amework, and combine large-scale data sets for training and validation. The experimental results show that the recognition accuracy of the proposed model in the noise environment improves by more than 15% compared with the traditional method, while significantly reducing the error rate.
Keyword:Speech Recognition Deep Learning Convolutional Neural Network
目 录
1绪论 1
1.1深度学习与语音识别的研究背景 1
1.2深度学习在语音识别中的意义分析 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2深度学习模型在语音识别中的应用 2
2.1深度神经网络的基本原理 2
2.2循环神经网络在语音建模中的作用 3
2.3卷积神经网络对声学特征的提取能力 3
2.4Transformer模型在端到端语音识别中的优势 4
2.5模型选择与优化策略 4
3声学建模的关键技术研究 5
3.1声学特征提取与预处理方法 5
3.2高斯混合模型与深度学习的结合 5
3.3端到端声学建模的技术挑战 6
3.4注意力机制在声学建模中的应用 6
3.5声学建模中的数据增强技术 7
4语言建模与解码优化研究 7
4.1语言模型的基本概念与分类 7
4.2基于深度学习的语言模型构建 8
4.3解码算法的优化与改进 8
4.4联合声学与语言建模的方法探讨 9
4.5实时语音识别中的解码效率提升 9
结论 10
参考文献 11
致谢 12
