摘 要
随着信息技术的迅猛发展，语音识别技术在智能交互、智能家居等领域发挥着日益重要的作用，深度学习模型为语音识别带来了新的发展机遇。本研究旨在评估深度学习模型在语音识别中的性能，选取卷积神经网络、循环神经网络和Transformer等典型深度学习模型，基于大规模语音数据集构建实验环境，采用准确率、召回率、F1值以及识别速度等多维度指标体系进行综合评估。研究结果表明，不同深度学习模型在语音识别性能上存在差异，其中Transformer模型在准确率方面表现最优，能够有效捕捉语音中的长依赖关系；卷积神经网络在特征提取方面具有优势，可快速定位语音的关键特征；循环神经网络对时序信息处理较好。本研究创新性地将多种深度学习模型在同一评价框架下进行对比分析，为后续研究提供了参考依据，有助于推动语音识别技术向更高水平发展，其成果对于优化语音识别系统架构、提升识别效果有着重要意义。
关键词：深度学习模型 语音识别 卷积神经网络
Abstract
With the rapid development of information technology, speech recognition technology is playing an increasingly important role in areas such as intelligent interaction and smart homes, and deep learning models have brought new opportunities for speech recognition. This study aims to evaluate the performance of deep learning models in speech recognition by selecting typical deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models. A large-scale speech dataset was utilized to construct the experimental environment, and a comprehensive evaluation was conducted using a multi-dimensional indicator system including accuracy, recall, F1 score, and recognition speed. The results indicate that there are differences in the performance of different deep learning models in speech recognition. Among them, the Transformer model performs optimally in terms of accuracy, effectively capturing long-range dependencies in speech; CNNs have advantages in feature extraction, capable of rapidly identifying key features in speech; RNNs handle sequential information well. Innovatively, this study compares multiple deep learning models within the same evaluation fr amework, providing a reference for future research and contributing to the advancement of speech recognition technology to higher levels. The findings are significant for optimizing the architecture of speech recognition systems and enhancing recognition performance.
Keyword:Deep Learning Model Speech Recognition Convolutional Neural Network
目 录
引言 1
1深度学习模型概述 1
1.1语音识别中的深度学习 1
1.2主流深度学习模型介绍 2
1.3模型选择依据与标准 2
2数据集与预处理方法 3
2.1语音数据集特点分析 3
2.2数据预处理技术应用 3
2.3特征提取与表示方法 4
3性能评估指标体系 4
3.1常用性能评估指标 5
3.2错误类型及成因分析 5
3.3多维度综合评价方法 6
4实验结果与对比分析 6
4.1不同模型实验设置 6
4.2性能对比与结果讨论 7
4.3影响因素及优化建议 7
结论 8
参考文献 9
致谢 9
