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.