  Self-supervised learning, such as with the wav2vec 2.0 framework
significantly improves the accuracy of end-to-end automatic speech recognition
(ASR). Wav2vec 2.0 has been applied to single-channel end-to-end ASR models. In
this work, we explored a self-supervised learning method for a multi-channel
end-to-end ASR model based on the wav2vec 2.0 framework. As the multi-channel
end-to-end ASR model, we focused on a multi-channel neural transducer. In
pre-training, we compared three different methods for feature quantization to
train a multi-channel conformer audio encoder: joint quantization, feature-wise
quantization and channel-wise quantization. In fine-tuning, we trained the
multi-channel conformer-transducer. All experiments were conducted using the
far-field in-house and CHiME-4 datasets. The results of the experiments showed
that feature-wise quantization was the most effective among the methods. We
observed a 66% relative reduction in character error rate compared with the
model without any pre-training for the far-field in-house dataset.
