Deep Attractor Network (DANet) is the state-of-the-art technique in speech
separation field, which uses Bidirectional Long Short-Term Memory (BLSTM), but
the complexity of the DANet model is very high. In this paper, a simplified and
powerful DANet model is proposed using Bidirectional Gated neural network
(BGRU) instead of BLSTM. The Gaussian Mixture Model (GMM) other than the
k-means was applied in DANet as a clustering algorithm to reduce the complexity
and increase the learning speed and accuracy. The metrics used in this paper
are Signal to Distortion Ratio (SDR), Signal to Interference Ratio (SIR),
Signal to Artifact Ratio (SAR), and Perceptual Evaluation Speech Quality (PESQ)
score. Two speaker mixture datasets from TIMIT corpus were prepared to evaluate
the proposed model, and the system achieved 12.3 dB and 2.94 for SDR and PESQ
scores respectively, which were better than the original DANet model. Other
improvements were 20.7% and 17.9% in the number of parameters and time
training, respectively. The model was applied on mixed Arabic speech signals
and the results were better than that in English.

提出了一种使用双向门控神经网络（BGRU）来简化和增强深度吸引子网络（DANet）模型的方法，使用高斯混合模型（GMM）作为聚类算法，以降低复杂度并提高学习速度和准确性。对 TIMIT 语料库中的混合语音数据集进行了实验评估，该模型在 SDR 和 PESQ 分数上分别达到 12.3 dB 和 2.94，表现优于原始 DANet 模型。