My baum-welch training is really slow! Is there something I can do to speed it up, apart from getting a faster processor?
A. In the first iteration, the models begin from flat distributions, and so the first iteration is usually very very slow. As the models get better in subsequent iterations, the training speeds up. There are other reasons why the iterations could be slow: the transcripts may not be force-aligned or the data may be noisy. For the same amount of training data, clean speech training gets done much faster than noisy speech training. The noisier the speech, the slower the training. If you have not force-aligned, the solution is to train CI models, force-align and retrain. If the data are noisy, try reducing the number of HMM states and/or not allowing skipped states in the HMM topology. Force-alignment also filters out bad transcripts and very noisy utterances.
