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So I was looking at SotA loss functions from a few years ago that weighted the CTC loss by the WER of the decoded phrase. Could we generalize the WER weighting

So I was looking at SotA loss functions from a few years ago that weighted the CTC loss by the WER of the decoded phrase. Could we generalize the WER weighting to optimize for the domain? Something like weight = w1 * WER + w2 * phonetic similarity + ... which also requires a hyperparameter search... But we are already dumping so many GPU hours here. I assume this is already being investigated by Google, though?

lunixbochs 5y ago
There are some a similar techniques where you use evaluation metrics to decide which data to train on each epoch. I wonder if you could make that parameter trainable instead of using a hyperparameter search for it. For phonetic similarity I've been playing with a dual objective system that could be promising.