Semi-supervised training for automatic speech recognition

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This was Manohar’s PhD dissertation at JHU.

Chapter 2 provides a relatively clear overview of how chain and non-chain models work in Kaldi.

In chapter 3 he tried using negative conditional entropy as the loss function for the unsupervised data, and it helped a bit.

In chapter 4 Manohar uses [CTC loss]/paper/ctc/.

In chapter 5, he discusses ways to do semi-supervised model training. It’s nice when you have parallel data in different domains, because then you can do a student-teacher model. When there’s no parallel data, the best you can do is decode the unsupervised data with the seed model and use that to train the LF-MMI model (see section 5.2.1).