  Continuous speech separation using a microphone array was shown to be
promising in dealing with the speech overlap problem in natural conversation
transcription. This paper proposes VarArray, an array-geometry-agnostic speech
separation neural network model. The proposed model is applicable to any number
of microphones without retraining while leveraging the nonlinear correlation
between the input channels. The proposed method adapts different elements that
were proposed before separately, including transform-average-concatenate,
conformer speech separation, and inter-channel phase differences, and combines
them in an efficient and cohesive way. Large-scale evaluation was performed
with two real meeting transcription tasks by using a fully developed
transcription system requiring no prior knowledge such as reference
segmentations, which allowed us to measure the impact that the continuous
speech separation system could have in realistic settings. The proposed model
outperformed a previous approach to array-geometry-agnostic modeling for all of
the geometry configurations considered, achieving asclite-based
speaker-agnostic word error rates of 17.5% and 20.4% for the AMI development
and evaluation sets, respectively, in the end-to-end setting using no
ground-truth segmentations.
