  Transcribing meetings containing overlapped speech with only a single distant
microphone (SDM) has been one of the most challenging problems for automatic
speech recognition (ASR). While various approaches have been proposed, all
previous studies on the monaural overlapped speech recognition problem were
based on either simulation data or small-scale real data. In this paper, we
extensively investigate a two-step approach where we first pre-train a
serialized output training (SOT)-based multi-talker ASR by using large-scale
simulation data and then fine-tune the model with a small amount of real
meeting data. Experiments are conducted by utilizing 75 thousand (K) hours of
our internal single-talker recording to simulate a total of 900K hours of
multi-talker audio segments for supervised pre-training. With fine-tuning on
the 70 hours of the AMI-SDM training data, our SOT ASR model achieves a word
error rate (WER) of 21.2% for the AMI-SDM evaluation set while automatically
counting speakers in each test segment. This result is not only significantly
better than the previous state-of-the-art WER of 36.4% with oracle utterance
boundary information but also better than a result by a similarly fine-tuned
single-talker ASR model applied to beamformed audio.
