超级M2M:监督和混合混合的共同学习,用于语音增强和抗噪声ASR
1Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, PR China.
概括
这项研究介绍了SuperM2M,这是一种用于神经语音增强的新型共同学习算法. 它直接在真实世界的数据上训练深度神经网络,显著改善了对更清晰的音频的概括.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 声学 声学 在声学上
背景情况:
- 主导神经语音增强依赖于模拟数据,导致现实世界的表现差.
- 在模拟数据上训练的模型对实际记录的数据的概括性是一个重大挑战.
- 现有的方法很难有效地适应各种音响环境.
研究的目的:
- 开发一种语音增强方法,直接在真实世界的数据上进行训练.
- 为了提高深度神经网络的通用性,用于语音增强.
- 提出一种新的共同学习算法,将监督和混合到混合方法结合起来.
主要方法:
- 通过模拟噪音作为单一源来进行语音增强的适应混合到混合 (M2M) 培训.
- 开发了一个共同学习算法 (SuperM2M),将M2M与监督学习集成在一起.
- 训练有素的深度神经网络 (DNN) 使用交替的真实和模拟数据的小批量,具有特定的损失函数.
主要成果:
- 超级M2M向真实记录的语音增强数据展示了改进的概括性.
- 共同学习方法有效地利用了现实和模拟数据进行培训.
- 对CHiME-4数据集的评估证实了拟议的SuperM2M算法的有效性.
结论:
- 直接在真实数据上的训练显著提高了语音增强模型的性能.
- 超M2M算法为强大的神经语音增强提供了一个有希望的方向.
- 这种方法对于需要高保真度音频处理的现实应用具有潜力.
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