Comments (1)
Because we want to maximize the MMI objective, instead of to minimize it.
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Related Issues (14)
- How to use CE regularizer in chain-model training? HOT 1
- Why is the chain loss computation so slow? HOT 8
- RIR format
- Problems about HCLG.fst needed by "train_transformer_se.py" HOT 2
- SETraining is extremely slow HOT 4
- minibatches for LFMMI HOT 1
- Decoding with train_transformer_ce.py HOT 2
- no module named 'kaldi.base._kaldi_error'
- Physical data format
- Selective data simulation
- Targeted simulation
- incompatible pytorch and torchvision version HOT 1
- Convolve vs. BlockConvolve for RIR Augmentation HOT 1
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