Speech enhancement is a demanding task in automated speech processing
pipelines, focusing on separating clean speech from noisy channels. Transformer
based models have recently bested RNN and CNN models in speech enhancement,
however at the same time they are much more computationally expensive and
require much more high quality training data, which is always hard to come by.
In this paper, we present an improvement for speech enhancement models that
maintains the expressiveness of self-attention while significantly reducing
model complexity, which we have termed Spectrum Attention Fusion. We carefully
construct a convolutional module to replace several self-attention layers in a
speech Transformer, allowing the model to more efficiently fuse spectral
features. Our proposed model is able to achieve comparable or better results
against SOTA models but with significantly smaller parameters (0.58M) on the
Voice Bank + DEMAND dataset.

在语音增强领域，我们提出了一种称为频谱注意力融合的方法，通过使用卷积模块替换自注意力层，提高了模型的计算效率，从而实现了与先进模型相媲美的结果，但参数规模更小（0.58M）的目标。