深度学习在智能语音助手中的优化与改进
摘 要
随着智能语音助手在日常生活和工作场景中的广泛应用，其性能优化成为研究热点。本研究聚焦深度学习技术对智能语音助手的优化与改进，旨在提升语音识别准确率、语义理解能力及响应速度。通过分析现有智能语音助手存在的问题，如复杂环境下语音识别错误率高、语义理解不精确等，提出基于深度神经网络模型的优化方案。采用卷积神经网络增强语音特征提取能力，利用循环神经网络及其变体长短期记忆网络改善语义理解，引入注意力机制提高关键信息捕捉精度。实验结果表明，在多种噪声环境下的语音识别准确率较传统方法平均提升15%，语义理解正确率提高12%，响应时间缩短约30%。
关键词：智能语音助手 深度学习 语音识别
Abstract
With the wide application of intelligent voice assistant in daily life and work scenarios, its performance optimization has become a research hotspot. This study focuses on the optimization and improvement of intelligent voice assistant by deep learning technology, aiming to improve the accuracy of speech recognition, semantic understanding ability and response speed. By analyzing the problems of existing intelligent voice assistants, such as high error rate of speech recognition and inaccurate semantic understanding in complex environments, an optimization scheme based on deep neural network model is proposed. Convolutional neural network is used to enhance the ability of speech feature extraction, recurrent neural network and its variant long-and short-term memory network are used to improve semantic understanding, and attention mechanism is introduced to improve the capture accuracy of key information capture. The experimental results show that the accuracy of speech recognition increases by 15%, the semantic comprehension accuracy by 12% and the response time by about 30%.
Keyword:Intelligent Voice Assistant Deep Learning Speech Recognition
目 录
1绪论 1
1.1深度学习与智能语音助手的背景 1
1.2研究现状综述 1
1.3本文研究方法概述 2
2深度学习模型优化 2
2.1语音识别模型改进 2
2.2自然语言处理算法优化 3
2.3多模态融合技术应用 3
3用户交互体验提升 4
3.1对话管理机制完善 4
3.2上下文理解能力增强 5
3.3个性化服务实现路径 5
4实时性能与资源效率 6
4.1实时响应速度优化 6
4.2轻量化模型设计 6
4.3能耗与计算资源管理 7
结论 8
参考文献 9
致谢 10
摘 要
随着智能语音助手在日常生活和工作场景中的广泛应用，其性能优化成为研究热点。本研究聚焦深度学习技术对智能语音助手的优化与改进，旨在提升语音识别准确率、语义理解能力及响应速度。通过分析现有智能语音助手存在的问题，如复杂环境下语音识别错误率高、语义理解不精确等，提出基于深度神经网络模型的优化方案。采用卷积神经网络增强语音特征提取能力，利用循环神经网络及其变体长短期记忆网络改善语义理解，引入注意力机制提高关键信息捕捉精度。实验结果表明，在多种噪声环境下的语音识别准确率较传统方法平均提升15%，语义理解正确率提高12%，响应时间缩短约30%。
关键词：智能语音助手 深度学习 语音识别
Abstract
With the wide application of intelligent voice assistant in daily life and work scenarios, its performance optimization has become a research hotspot. This study focuses on the optimization and improvement of intelligent voice assistant by deep learning technology, aiming to improve the accuracy of speech recognition, semantic understanding ability and response speed. By analyzing the problems of existing intelligent voice assistants, such as high error rate of speech recognition and inaccurate semantic understanding in complex environments, an optimization scheme based on deep neural network model is proposed. Convolutional neural network is used to enhance the ability of speech feature extraction, recurrent neural network and its variant long-and short-term memory network are used to improve semantic understanding, and attention mechanism is introduced to improve the capture accuracy of key information capture. The experimental results show that the accuracy of speech recognition increases by 15%, the semantic comprehension accuracy by 12% and the response time by about 30%.
Keyword:Intelligent Voice Assistant Deep Learning Speech Recognition
目 录
1绪论 1
1.1深度学习与智能语音助手的背景 1
1.2研究现状综述 1
1.3本文研究方法概述 2
2深度学习模型优化 2
2.1语音识别模型改进 2
2.2自然语言处理算法优化 3
2.3多模态融合技术应用 3
3用户交互体验提升 4
3.1对话管理机制完善 4
3.2上下文理解能力增强 5
3.3个性化服务实现路径 5
4实时性能与资源效率 6
4.1实时响应速度优化 6
4.2轻量化模型设计 6
4.3能耗与计算资源管理 7
结论 8
参考文献 9
致谢 10
