Perform forced alignment and train acoustic model on separate audio files

[kmc]

I'm using the LibriSpeech s5 recipe to train an acoustic model on reverberant speech using sentences in the train-clean-100 dataset (which I convolved with room impulse responses). Papers I've read (e.g. the ASpIRE paper) suggest training a GMM-HMM based system to perform forced alignment on the anechoic sentences, and then training a neural network based acoustic model on the corresponding reverberant sentences using the clean alignments as labels. I'd like to do the same but use a GMM-HMM acoustic model instead of a neural network.

Is it possible to train a GMM-HMM acoustic model on reverberant sentences using alignments generated from a GMM-HMM forced aligner trained on the corresponding anechoic sentences? If so, how would I accomplish this?

[Daniel Povey]

Look at the ami recipes, in particular for files that contain the keyword "ihmali"

On Thu, Aug 8, 2019 at 9:18 AM kmc <kmc...@duke.edu> wrote:

I'm using the LibriSpeech s5 recipe to train an acoustic model on reverberant speech using sentences in the train-clean-100 dataset (which I convolved with room impulse responses). Papers I've read (e.g. the ASpIRE paper) suggest training a GMM-HMM based system to perform forced alignment on the anechoic sentences, and then training a neural network based acoustic model on the corresponding reverberant sentences using the clean alignments as labels. I'd like to do the same but use a GMM-HMM acoustic model instead of a neural network.

Is it possible to train a GMM-HMM acoustic model on reverberant sentences using alignments generated from a GMM-HMM forced aligner trained on the corresponding anechoic sentences? If so, how would I accomplish this?

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