  This paper presents a novel streaming automatic speech recognition (ASR)
framework for multi-talker overlapping speech captured by a distant microphone
array with an arbitrary geometry. Our framework, named t-SOT-VA, capitalizes on
independently developed two recent technologies; array-geometry-agnostic
continuous speech separation, or VarArray, and streaming multi-talker ASR based
on token-level serialized output training (t-SOT). To combine the best of both
technologies, we newly design a t-SOT-based ASR model that generates a
serialized multi-talker transcription based on two separated speech signals
from VarArray. We also propose a pre-training scheme for such an ASR model
where we simulate VarArray's output signals based on monaural single-talker ASR
training data. Conversation transcription experiments using the AMI meeting
corpus show that the system based on the proposed framework significantly
outperforms conventional ones. Our system achieves the state-of-the-art word
error rates of 13.7% and 15.5% for the AMI development and evaluation sets,
respectively, in the multiple-distant-microphone setting while retaining the
streaming inference capability.
