Multi-speakers Speech Separation Based on Modified Attractor Points Estimation and GMM Clustering

Shanfa Ke, Ruimin Hu, Gang Li, Tingzhao Wu, Xiaochen Wang, Zhongyuan Wang

Abstract: In this paper, a new attractor points estimation method for DANet algorithm used in single channel multi-speaker speech separation has been proposed. A prerequisite is that there must be separate segments of each source in the mixture. This condition is met in the actual situation because the source signal is not overlapping at any time. With this prerequisite, an isolated source segments extracted from the mixture is converted to the embedding space. With the embedding of isolated source segments, a more accurately attractor point for each source will be created, due to it does not contain components of other sources. In addition, a gaussian mixture model(GMM) clustering method instead of K-means clustering method were used at run time. The experiment demonstrated that the proposed method gets a better separation performance than state of the art method up to 1.04dB in SDR.