  Speech recognition and other natural language tasks have long benefited from
voting-based algorithms as a method to aggregate outputs from several systems
to achieve a higher accuracy than any of the individual systems. Diarization,
the task of segmenting an audio stream into speaker-homogeneous and co-indexed
regions, has so far not seen the benefit of this strategy because the structure
of the task does not lend itself to a simple voting approach. This paper
presents DOVER (diarization output voting error reduction), an algorithm for
weighted voting among diarization hypotheses, in the spirit of the ROVER
algorithm for combining speech recognition hypotheses. We evaluate the
algorithm for diarization of meeting recordings with multiple microphones, and
find that it consistently reduces diarization error rate over the average of
results from individual channels, and often improves on the single best channel
chosen by an oracle.
