Title: CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments,
Abstract: Casual conversations involving multiple speakers and noises from surrounding
devices are part of everyday environments and pose challenges for automatic
speech recognition systems. These challenges in speech recognition are target
for the CHiME-5 challenge. In the present study, an attempt is made to overcome
these challenges by employing a convolutional neural network (CNN)-based
multichannel end-to-end speech recognition system. The system comprises an
attention-based encoder-decoder neural network that directly generates a text
as an output from a sound input. The mulitchannel CNN encoder, which uses
residual connections and batch renormalization, is trained with augmented data,
including white noise injection. The experimental results show that the word
error rate (WER) was reduced by 11.9% absolute from the end-to-end baseline.