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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.
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Leveraging Sound Localization to Improve Continuous Speaker Separation
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.
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- Award ID(s):
- 2125074
- PAR ID:
- 10552806
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-4485-1
- Page Range / eLocation ID:
- 621 to 625
- Subject(s) / Keyword(s):
- MIMO complex spectral mapping continuous speaker separation robust speaker localization
- Format(s):
- Medium: X
- Location:
- Seoul, Korea, Republic of
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP48485.2024.10446934
