  Joint optimization of multi-channel front-end and automatic speech
recognition (ASR) has attracted much interest. While promising results have
been reported for various tasks, past studies on its meeting transcription
application were limited to small scale experiments. It is still unclear
whether such a joint framework can be beneficial for a more practical setup
where a massive amount of single channel training data can be leveraged for
building a strong ASR back-end. In this work, we present our investigation on
the joint modeling of a mask-based beamformer and
Attention-Encoder-Decoder-based ASR in the setting where we have 75k hours of
single-channel data and a relatively small amount of real multi-channel data
for model training. We explore effective training procedures, including a
comparison of simulated and real multi-channel training data. To guide the
recognition towards a target speaker and deal with overlapped speech, we also
explore various combinations of bias information, such as direction of arrivals
and speaker profiles. We propose an effective location bias integration method
called deep concatenation for the beamformer network. In our evaluation on
various meeting recordings, we show that the proposed framework achieves a
substantial word error rate reduction.
