Online Neural Speaker Diarization With Target Speaker Tracking

This paper proposes an online target speaker voice activity detection (TS-VAD) system for speaker diarization tasks that does not rely on prior knowledge from clustering-based diarization systems to obtain target speaker embeddings. By adapting conventional TS-VAD for real-time operation, our framework identifies speaker activities using self-generated embeddings, ensuring consistent performance and avoiding permutation inconsistencies during inference. In the inference phase, we employ a front-end model to extract frame-level speaker embeddings for each incoming signal block. Subsequently, we predict each speaker's detection state based on these frame-level embeddings and the previously estimated target speaker embeddings. The target speaker embeddings are then updated by aggregating the frame-level embeddings according to the current block's predictions. Our model predicts results block-by-block and iteratively updates target speaker embeddings until reaching the end of the signal. Experimental results demonstrate that the proposed method outperforms offline clustering-based diarization systems on the DIHARD III and AliMeeting datasets. Additionally, this approach is extended to multi-channel data, achieving comparable performance to state-of-the-art offline diarization systems.