  Speaker diarization systems segment a conversation recording based on the
speakers' identity. Such systems can misclassify the speaker of a portion of
audio due to a variety of factors, such as speech pattern variation, background
noise, and overlapping speech. These errors propagate to, and can adversely
affect, downstream systems that rely on the speaker's identity, such as
speaker-adapted speech recognition. One of the ways to mitigate these errors is
to provide segment-level diarization confidence scores to downstream systems.
In this work, we investigate multiple methods for generating diarization
confidence scores, including those derived from the original diarization system
and those derived from an external model. Our experiments across multiple
datasets and diarization systems demonstrate that the most competitive
confidence score methods can isolate ~30% of the diarization errors within
segments with the lowest ~10% of confidence scores.
