基于多模式学习的语音增强和分离,最近的创新,新的视野,挑战和现实世界的应用
Rizwan Ullah1, Shaohui Zhang2, Muhammad Asif3
1School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, PR China; School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, PR China.
Computers in biology and medicine
|April 2, 2025
概括
多模式学习通过整合音频,视觉和文本数据来增强语音增强和分离. 这种方法解决了噪音环境中的挑战,并为辅助技术和其他领域提供了新的可能性.
科学领域:
- 语音处理 语音处理
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 听力损失是全球日益严重的问题,需要先进的语音增强技术.
- 多模式学习,集成音频,视觉和文本数据,显示出对语音增强和分离的重大承诺.
- 当前的挑战包括模式同步,模型稳定性和实时可扩展性.
研究的目的:
- 为提供多式联络学习进行语音增强和分离的综合性审查.
- 突出现有方法在现实世界条件下的局限性.
- 探索多式联运系统如何利用各种数据来提高性能.
主要方法:
- 对深度学习架构的审查:变压器,CNN,GNN,VAE,GAN和扩散模型.
- 融合策略的探索:早期融合,晚期融合和注意力机制.
- 分析多式输入,包括唇部运动,文本转录和大脑信号.
主要成果:
- 多式联络系统有效地利用互补信息来增强语音处理.
- 各种深度学习模型和融合策略表明了提高性能的潜力.
- 成功的应用包括自动驾驶汽车,心理健康监测,增强现实,公共安全,远程医疗和助听器.
结论:
- 多模式学习是一种用于语音增强和分离的变革性策略.
- 解决目前的局限性是释放这些技术全部潜力的关键.
- 概述了未来的研究方向,以指导这一领域的进步.
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