论文标题
语音修复和回响时间缩短目标
Speech Dereverberation with A Reverberation Time Shortening Target
论文作者
论文摘要
这项工作提出了一个基于回响时间缩短（RTS）的新学习目标，用于语音覆盖。通常将覆盖的学习目标设置为直接路径演讲，或者在某些早期反思中选择。这种类型的目标突然截断了混响，因此可能不适合网络培训。拟议的RTS目标抑制了混响，同时保持了回响的指数衰减特性，这将减轻网络训练，从而减少由预测误差引起的信号失真。此外，这项工作在实验上研究了，以使我们先前提议的全鞋语音deonoing网络对语音覆盖。实验表明，在更好地抑制混响和信号失真方面，RTS比直接路径语音和早期反射更合适。 FullSubnet能够实现出色的替代性能。
This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training. The proposed RTS target suppresses reverberation and meanwhile maintains the exponential decaying property of reverberation, which will ease the network training, and thus reduce signal distortion caused by the prediction error. Moreover, this work experimentally study to adapt our previously proposed FullSubNet speech denoising network to speech dereverberation. Experiments show that RTS is a more suitable learning target than direct-path speech and early reflections, in terms of better suppressing reverberation and signal distortion. FullSubNet is able to achieve outstanding dereverberation performance.
