Multi-scale Speaker Diarization with Dynamic Scale Weighting

Tae Jin Park; Nithin Rao Koluguri; Jagadeesh Balam; Boris Ginsburg

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arXiv · 2203.15974

Multi-scale Speaker Diarization with Dynamic Scale Weighting

Tae Jin Park·Nithin Rao Koluguri·Jagadeesh Balam·Boris Ginsburg

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Abstract

Speaker diarization systems are challenged by a trade-off between the temporal resolution and the fidelity of the speaker representation. By obtaining a superior temporal resolution with an enhanced accuracy, a multi-scale approach is a way to cope with such a trade-off. In this paper, we propose a more advanced multi-scale diarization system based on a multi-scale diarization decoder. There are two main contributions in this study that significantly improve the diarization performance. First, we use multi-scale clustering as an initialization to estimate the number of speakers and obtain the average speaker representation vector for each speaker and each scale. Next, we propose the use of 1-D convolutional neural networks that dynamically determine the importance of each scale at each time step. To handle a variable number of speakers and overlapping speech, the proposed system can estimate the number of existing speakers. Our proposed system achieves a state-of-art performance on the CALLHOME and AMI MixHeadset datasets, with 3.92% and 1.05% diarization error rates, respectively.

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Tae Jin Park, Nithin Rao Koluguri, Jagadeesh Balam, Boris Ginsburg. 2022-03-30. Multi-scale Speaker Diarization with Dynamic Scale Weighting. https://arxiv.org/abs/2203.15974

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