三重0:在一个端到端的冷无声语音分离网络上,零射击拒绝和脱声
Sania Gul1,2, Muhammad Salman Khan3, Ata Ur-Rehman4,5
1Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan.
PloS one
|July 16, 2024
概括
这项研究介绍了一种新的语音增强方法,使用冷的深度学习网络来消除和消除声音. 该方法在零射击测试中实现了令人满意的性能,在某些条件下超过传统方法.
科学领域:
- 数字信号处理 数字信号处理
- 机器学习 机器学习
- 声学 声学 在声学方面
背景情况:
- 深度学习显著提升了语音增强,但需要大量资源.
- 转移学习减少了培训需求,但通常需要微调.
- 现有的方法在复杂的声环境中难以同时消除噪声和消除反响.
研究的目的:
- 提出一种新的语音消音和消音 (SD&D) 方法.
- 为了利用SD&D无需微调的端到端冷双耳语音分离网络来实现SD&D.
- 在零射击测试场景中评估方法的有效性.
主要方法:
- 使用预训练的,冷的双耳无声语音分离网络.
- 从噪音和反响条件输入网络的音声间线索.
- 在未见的噪音和反响语音数据上进行零射击测试 (ZST).
主要成果:
- 结网络在ZST中实现了令人满意的SD&D性能,而没有先前接触到此类条件.
- 在客观语音质量和可理解度指标上超过了加权预测错误 (WPE) 算法.
- 演示了与深度学习SD&D算法可比的性能,并在不同的数据集上提高了对WPE的可理解性.
结论:
- 一个结网络可以有效地在ZST中执行SD&D,从而消除了对微调的需求.
- 拟议的方法为语音增强提供了一种计算效率高和资源节约的方法.
- 这种技术对现实世界应用具有前途,在不利的声学条件下需要强大的语音分离.
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