Search for: All records

Continuous speaker separation aims to separate overlapping speakers in real-world environments like meetings, but it often falls short in isolating speech segments of a single speaker. This leads to split signals that adversely affect downstream applications such as automatic speech recognition and speaker diarization. Existing solutions like speaker counting have limitations. This paper presents a novel multi-channel approach for continuous speaker separation based on multi-input multi-output (MIMO) complex spectral mapping. This MIMO approach enables robust speaker localization by preserving inter-channel phase relations. Speaker localization as a byproduct of the MIMO separation model is then used to identify single-talker frames and reduce speaker splitting. We demonstrate that this approach achieves superior frame-level sound localization. Systematic experiments on the LibriCSS dataset further show that the proposed approach outperforms other methods, advancing state-of-the-art speaker separation performance. 

Current deep learning based multi-channel speaker sepa- ration methods produce a monaural estimate of speaker sig- nals captured by a reference microphone. This work presents a new multi-channel complex spectral mapping approach that simultaneously estimates the real and imaginary spectrograms of all speakers at all microphones. The proposed multi-input multi-output (MIMO) separation model uses a location-based training (LBT) criterion to resolve the permutation ambiguity in talker-independent speaker separation across microphones. Experimental results show that the proposed MIMO separation model outperforms a multi-input single-output (MISO) speaker separation model with monaural estimates. We also combine the MIMO separation model with a beamformer and a MISO speech enhancement model to further improve separation performance. The proposed approach achieves the state-of-the-art speaker separation on the open LibriCSS dataset.