  In this paper, we propose a dual-encoder ASR architecture for joint modeling
of close-talk (CT) and far-talk (FT) speech, in order to combine the advantages
of CT and FT devices for better accuracy. The key idea is to add an encoder
selection network to choose the optimal input source (CT or FT) and the
corresponding encoder. We use a single-channel encoder for CT speech and a
multi-channel encoder with Spatial Filtering neural beamforming for FT speech,
which are jointly trained with the encoder selection. We validate our approach
on both attention-based and RNN Transducer end-to-end ASR systems. The
experiments are done with conversational speech from a medical use case, which
is recorded simultaneously with a CT device and a microphone array. Our results
show that the proposed dual-encoder architecture obtains up to 9% relative WER
reduction when using both CT and FT input, compared to the best single-encoder
system trained and tested in matched condition.
