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Speaker embedding has been a fundamental feature for speaker-related taskssuch as verification, clustering, and diarization. Traditionally, speakerembeddings are represented as fixed vectors in high-dimensional space. Thiscould lead to biased estimations, especially when handling shorter utterances.In this paper we propose to represent a speaker utterance as "floating" vectorwhose state is indeterminate without knowing the context. The state of aspeaker representation is jointly determined by itself, other speech from thesame speaker, as well as other speakers it is being compared to. The content ofthe speech also contributes to determining the final state of a speakerrepresentation. We pre-train an indeterminate speaker representation model thatestimates the state of an utterance based on the context. The pre-trained modelcan be fine-tuned for downstream tasks such as speaker verification, speakerclustering, and speaker diarization. Substantial improvements are observedacross all downstream tasks.

PRISM: Pre-trained Indeterminate Speaker Representation Model for Speaker Diarization and Speaker Verification