Scaling Up Online Speech Recognition Using ConvNets
Table 3: Effect of future context on WER performance
['[BOLD] Future context', '[BOLD] vid-clean WER', '[BOLD] vid-noisy WER']
We have kept the receptive field of our convolutional acoustic model fixed at 10 seconds, use local normalization and remove time from layer normalization axes for all the experiments. We see that the WER almost always improves as the future context size is increases. We see that the transcripts can be produced faster by using smaller future context sizes.