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In this paper, we propose a dual-encoder ASR architecture for joint modelingof close-talk (CT) and far-talk (FT) speech, in order to combine the advantagesof CT and FT devices for better accuracy. The key idea is to add an encoderselection network to choose the optimal input source (CT or FT) and thecorresponding encoder. We use a single-channel encoder for CT speech and amulti-channel encoder with Spatial Filtering neural beamforming for FT speech,which are jointly trained with the encoder selection. We validate our approachon both attention-based and RNN Transducer end-to-end ASR systems. Theexperiments are done with conversational speech from a medical use case, whichis recorded simultaneously with a CT device and a microphone array. Our resultsshow that the proposed dual-encoder architecture obtains up to 9% relative WERreduction when using both CT and FT input, compared to the best single-encodersystem trained and tested in matched condition.

Dual-Encoder Architecture with Encoder Selection for Joint Close-Talk and Far-Talk Speech Recognition