  This paper proposes an online target speaker voice activity detection system
for speaker diarization tasks, which does not require a priori knowledge from
the clustering-based diarization system to obtain the target speaker
embeddings. By adapting the conventional target speaker voice activity
detection for real-time operation, this framework can identify speaker
activities using self-generated embeddings, resulting in consistent performance
without permutation inconsistencies in the inference phase. During the
inference process, we employ a front-end model to extract the frame-level
speaker embeddings for each coming block of a signal. Next, we predict the
detection state of each speaker based on these frame-level speaker embeddings
and the previously estimated target speaker embedding. Then, the target speaker
embeddings are updated by aggregating these frame-level speaker embeddings
according to the predictions in the current block. Our model predicts the
results for each block and updates the target speakers' embeddings until
reaching the end of the signal. Experimental results show that the proposed
method outperforms the offline clustering-based diarization system on the
DIHARD III and AliMeeting datasets. The proposed method is further extended to
multi-channel data, which achieves similar performance with the
state-of-the-art offline diarization systems.
