ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
Table 6: Comparing ContextNet with previous best results on Youtube test sets
['[BOLD] Model', '[BOLD] #Params (M)', '[BOLD] GFLOPS', '[BOLD] Youtube WER']
Finally, we show that the proposed architecture is also effective on large scale datasets. We use a experiment setup similar to [chiu20longform], where the training set has public Youtube videos with semi-supervised transcripts generated by the approach in [liao2013large]. We evaluate on 117 videos with a total duration of 24.12 hours. We can see that ContextNet outperforms the previous best architecture from [chiu20longform], which is a combination of convolution and bidirectional LSTM, by 12% relatively with fewer parameters and FLOPS.