Speakerfilter: Deep Learning-Based Target Speaker Extraction Using Anchor Speech

Speaker extraction aims to separate a target speaker from multiple voices which is useful for applications, e.g. teleconference. In many practical cases, it has an opportunity to get a piece voice of the target speaker in advance, which provides useful information for speaker extraction. This paper addresses the problem of extracting the target speaker from the mixture using a short piece of anchor speech. To effectively utilize anchor speech, we propose a multi-level feature extraction and seamlessly integrate the features into a speech separation model. Experiments are conducted on the two-speaker dataset (WSJ0-mix2) which is widely used for speaker extraction. The systematic evaluation shows that the proposed method significantly outperforms the previous methods and achieves a signal-to-distortion ratio (SDR) improvement of 11.3 dB on the unprocessed mixture.