Copyright 2022 Zhengjun Yue, Feifei Xiong, Heidi Christensen, Jon Barker

This is a Kaldi recipe to build automatic speech recognition systems on the Torgo corpus of dysarthric speech.

Update the KALDI_ROOT and DATA_ORIG variables in path.sh to point to the correct locations for your Kaldi installation and the Torgo corpus. Then run the following:

source path.sh
ln -s $KALDI_ROOT/egs/wsj/s5/{steps,utils} .

Some scripts in local/ also require the following Python packages:

invoke numpy pandas python-Levenshtein

The following instructions allow to train ASR systems on Torgo and to reproduce results from the paper.

# HMM/GMM systems + LF-MMI (TDNN-F) systems:
./run.sh

# Show WER:
./local/get_wer.py exp/sgmm

Please cite the following paper if you use this script for your research or are interested in this paper.

@inproceedings{yue2020exploring,
  title={Exploring appropriate acoustic and language modelling choices for continuous dysarthric speech recognition},
  author={Yue, Zhengjun and Xiong, Feifei and Christensen, Heidi and Barker, Jon},
  booktitle={In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020},
  pages={6094--6098},
  year={2020},
  organization={IEEE}
}

The code is based on an earlier recipe by Cristina España-Bonet and José A. R. Fonollosa.