论文标题
推论和denoise：基于因果推理的神经语音增强
Inference and Denoise: Causal Inference-based Neural Speech Enhancement
论文作者
论文摘要
这项研究通过将噪声的存在建模为干预措施来解决因果推理范式内的语音增强（SE）任务。基于潜在的结果框架，提出的基于因果推理的语音增强（CISE）在使用噪声探测器的间隔噪声语音中将干净和嘈杂的框架分开，并将两组帧分配给两个基于掩模的增强模块（EMS）以执行噪声差异。具体而言，我们使用噪声的存在作为训练过程中EM选择的指导，噪声检测器根据每个帧的噪声存在选择增强模块。此外，我们得出了SE特定的平均治疗效果，以充分量化因果关系。实验证据表明，CISE在研究环境中的表现优于基于非因果的SE方法，并且比更复杂的SE模型具有更好的性能和效率。
This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. Based on the potential outcome framework, the proposed causal inference-based speech enhancement (CISE) separates clean and noisy frames in an intervened noisy speech using a noise detector and assigns both sets of frames to two mask-based enhancement modules (EMs) to perform noise-conditional SE. Specifically, we use the presence of noise as guidance for EM selection during training, and the noise detector selects the enhancement module according to the prediction of the presence of noise for each frame. Moreover, we derived a SE-specific average treatment effect to quantify the causal effect adequately. Experimental evidence demonstrates that CISE outperforms a non-causal mask-based SE approach in the studied settings and has better performance and efficiency than more complex SE models.
