  Speaker embedding has been a fundamental feature for speaker-related tasks
such as verification, clustering, and diarization. Traditionally, speaker
embeddings are represented as fixed vectors in high-dimensional space. This
could lead to biased estimations, especially when handling shorter utterances.
In this paper we propose to represent a speaker utterance as "floating" vector
whose state is indeterminate without knowing the context. The state of a
speaker representation is jointly determined by itself, other speech from the
same speaker, as well as other speakers it is being compared to. The content of
the speech also contributes to determining the final state of a speaker
representation. We pre-train an indeterminate speaker representation model that
estimates the state of an utterance based on the context. The pre-trained model
can be fine-tuned for downstream tasks such as speaker verification, speaker
clustering, and speaker diarization. Substantial improvements are observed
across all downstream tasks.
