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Overlapping speech diarization is always treated as a multi-labelclassification problem. In this paper, we reformulate this task as asingle-label prediction problem by encoding the multi-speaker labels with powerset. Specifically, we propose the speaker embedding-aware neural diarization(SEND) method, which predicts the power set encoded labels according to thesimilarities between speech features and given speaker embeddings. Our methodis further extended and integrated with downstream tasks by utilizing thetextual information, which has not been well studied in previous literature.The experimental results show that our method achieves lower diarization errorrate than the target-speaker voice activity detection. When textual informationis involved, the diarization errors can be further reduced. For the realmeeting scenario, our method can achieve 34.11% relative improvement comparedwith the Bayesian hidden Markov model based clustering algorithm.

Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual Information