摘 要
语音识别作为人工智能领域的重要分支，近年来随着深度学习技术的发展取得了显著进步。在研究背景方面，传统语音识别方法面临诸多挑战，如对复杂环境适应性差、识别准确率有待提高等，而深度学习凭借其强大的特征提取与表达能力为语音识别带来新的发展机遇。研究目的在于探索深度学习算法如何有效提升语音识别性能，改善用户体验并拓展应用场景。为此采用多种深度学习模型进行实验，包括卷积神经网络（CNN）用于局部特征捕捉，循环神经网络（RNN）及其变体长短期记忆网络（LSTM）处理序列信息，以及注意力机制增强关键信息聚焦能力。结果表明，基于深度学习的语音识别系统在多种测试集上均展现出更高的准确率和鲁棒性，尤其在嘈杂环境下优势明显。创新点在于将多模态数据融合进深度学习框架，通过结合视觉、文本等辅助信息进一步优化识别效果；同时提出一种新型损失函数以解决类别不平衡问题，从而提高小概率事件识别精度。主要贡献是推动了语音识别技术从实验室走向实际应用，在智能家居、智能客服等领域实现大规模商用，并为后续研究提供了新思路与方法借鉴。
关键词：深度学习；语音识别；卷积神经网络；长短期记忆网络；多模态数据融合
Abstract
Speech recognition, as a crucial branch of artificial intelligence, has witnessed significant advancements in recent years with the development of deep learning technologies. Traditional speech recognition methods have faced numerous challenges such as poor adaptability to complex environments and the need for improved recognition accuracy. Deep learning, however, offers new opportunities for speech recognition through its powerful feature extraction and representation capabilities. This study aims to explore how deep learning algorithms can effectively enhance speech recognition performance, improve user experience, and expand application scenarios. To achieve this, various deep learning models were employed in experiments, including Convolutional Neural Networks (CNNs) for local feature capture, Recurrent Neural Networks (RNNs) and their variant Long Short-Term Memory networks (LSTMs) for sequence information processing, as well as attention mechanisms to enhance the focus on critical information. The results indicate that deep learning-based speech recognition systems exhibit higher accuracy and robustness across multiple test sets, particularly in noisy environments. An innovation lies in integrating multimodal data into the deep learning fr amework, optimizing recognition outcomes by combining auxiliary information from visual and textual sources. Additionally, a novel loss function was proposed to address class imbalance issues, thereby improving the recognition accuracy of low-probability events. The primary contribution is advancing speech recognition technology from laboratory settings to practical applications, achieving large-scale commercialization in areas such as smart homes and intelligent customer service, while providing new insights and methodological references for future research.
Keywords：Deep Learning；Speech Recognition；Convolutional Neural Network；Long Short-Term Memory Network；Multimodal Data Fusion
目 录
摘 要 I
Abstract II
引 言 1
第一章 深度学习模型架构进展 2
1.1 卷积神经网络的应用 2
1.2 循环神经网络的优化 2
1.3 变分自编码器的作用 3
第二章 语音特征提取技术革新 5
2.1 端到端特征学习方法 5
2.2 预训练模型的迁移 5
2.3 多模态特征融合策略 6
第三章 噪声环境下的鲁棒性提升 8
3.1 数据增强技术应用 8
3.2 自适应噪声抑制算法 8
3.3 增强学习在降噪中的作用 9
第四章 实时处理与效率优化 11
4.1 轻量化模型设计思路 11
4.2 并行计算框架构建 11
4.3 边缘计算的集成方案 12
结 论 14
参考文献 15
致 谢 16
