  The field of speech recognition is in the midst of a paradigm shift:
end-to-end neural networks are challenging the dominance of hidden Markov
models as a core technology. Using an attention mechanism in a recurrent
encoder-decoder architecture solves the dynamic time alignment problem,
allowing joint end-to-end training of the acoustic and language modeling
components. In this paper we extend the end-to-end framework to encompass
microphone array signal processing for noise suppression and speech enhancement
within the acoustic encoding network. This allows the beamforming components to
be optimized jointly within the recognition architecture to improve the
end-to-end speech recognition objective. Experiments on the noisy speech
benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system
outperformed the attention-based baseline with input from a conventional
adaptive beamformer.
