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This work proposes a frame-wise online/streaming end-to-end neuraldiarization (FS-EEND) method in a frame-in-frame-out fashion. To frame-wiselydetect a flexible number of speakers and extract/update their correspondingattractors, we propose to leverage a causal speaker embedding encoder and anonline non-autoregressive self-attention-based attractor decoder. A look-aheadmechanism is adopted to allow leveraging some future frames for effectivelydetecting new speakers in real time and adaptively updating speaker attractors.The proposed method processes the audio stream frame by frame, and has a lowinference latency caused by the look-ahead frames. Experiments show that,compared with the recently proposed block-wise online methods, our methodFS-EEND achieves state-of-the-art diarization results, with a low inferencelatency and computational cost.

Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors