Well for example, some comparisons to the CNN paper you pointed to: - No comparison is given of number of model parameters. If optimizing strictly for model si

Well for example, some comparisons to the CNN paper you pointed to: - No comparison is given of number of model parameters. If optimizing strictly for model size, RNNs tend to be nice and compact. - The computational advantage of the CNN at training time is throughput. The advantage of RNN at decoding time is streaming latency. Running the CNN frame by frame as they are received removes the ability to run frames in parallel and if the CNN is larger, it will run slower, and depending on its receptive fields it may not even stream well at all. - That particular CNN system uses a strictly external LM that is not jointly trained and has an additional hyper parameter at decoding time to weight the LM that requires additional tuning. - It is still autoregressive in the beam search, so the LM will still be run many times sequentially adding tokens just like an RNN LM, and is likely to be more expensive. The throughput advantage a conv lm has in scoring whole sentences is totally lost. In fact, there doesn't seem to be anything special about the choice of a conv lm for that paper except that it is fun to make all the parts convolutional. - CNNs frequently require more total flops, but are high throughput on eg a GPU because they expose so much parallelism. On an embedded CPU this can be a bad tradeoff. As a side note, there's no reason that CNN architecture, which in the paper is trained with a close relative of CTC and is decoded identically to a RNN CTC AM + external LM, couldn't be trained as an RNN transducer. Despite the name neither the am nor lm have to actually be RNNs.