Kaldi implementation of a neural network phone duration model, as described in the paper:
Tanel Alumäe. Neural network phone duration model for speech recognition. Interspeech 2014, Singapore. https://phon.ioc.ee/dokuwiki/lib/exe/fetch.php?media=people:tanel:icassp2014-durmodel.pdf
The duration model has been tested on English, Estonian and Finnish.
We provide a recipe for the TEDLIUM dataset. The baseline system uses online multisplice speed-perturbed DNN models, with rescoring of the lattices with large language model from Cantab Research. Duration model decreases WER from 12.7% to 12.1% for the development set and from 11.7% to 11.0% for the test set.
- Python 2.6 (with argparse) or 2.7
- Theano
- Pylearn2
- sklearn
Theano can be installed using python's pip utility:
pip install Theano --user
This installs Theano locally (not systemwide). More instructions: http://deeplearning.net/software/theano/install.html
Pylearn2 should be cloned from Github, see http://deeplearning.net/software/pylearn2/#download-and-installation
See run_tedlium.sh for a sample script that trains a duration model on TEDLIUM data, on top of already trained DNN models.
Duration model is trained using Pylearn2 that itself uses Theano. Theano can use GPU which makes the training much faster (takes about 1 hour on TEDLIUM data, when using a Tesla K20). You should use the ~/.theanorc file to instruct Theano to use the GPU:
[global]
device = gpu
floatX = float32
If you use a cluster, you should also instruct Theano to use a machine-local temporary directory for its compilation directory. Set the following line in the [global] section of the .theanorc file:
base_compiledir=/tmp/%(user)s/theano.NOBACKUP
All language specific details are defined in dur-model/python/lat-model/data/languages.yaml.
You can cite the following paper if you use this software:
@InProceedings{alumae2014,
author={Alum\"{a}e, Tanel},
title={Neural network phone duration model for speech recognition},
booktitle={Interspeech 2014},
address={Singapore},
year=2014
}