During the last decades, automatic speech processing systems witnessed
an important progress and achieved remarkable reliability. As a result,
such technologies have been exploited in new areas and applications
including medical practice. In disordered speech evaluation context,
perceptual evaluation is still the most common method used in clinical
practice for the diagnosing and the following of the condition progression
of patients despite its well documented limits (such as subjectivity).
In this paper, we propose an automatic approach for the prediction
of dysarthric speech evaluation metrics (intelligibility, severity,
articulation impairment) based on the representation of the speech
acoustics in the total variability subspace based on the i-vectors
paradigm. The proposed approach, evaluated on 129 French dysarthric
speakers from the DesPhoAPady and VML databases, is proven to be
efficient for the modeling of patient’s production and capable
of detecting the evolution of speech quality. Also, low RMSE and high
correlation measures are obtained between automatically predicted metrics
and perceptual evaluations.
Cite as: Laaridh, I., Kheder, W.B., Fredouille, C., Meunier, C. (2017) Automatic Prediction of Speech Evaluation Metrics for Dysarthric Speech. Proc. Interspeech 2017, 1834-1838, doi: 10.21437/Interspeech.2017-1363
@inproceedings{laaridh17_interspeech,
author={Imed Laaridh and Waad Ben Kheder and Corinne Fredouille and Christine Meunier},
title={{Automatic Prediction of Speech Evaluation Metrics for Dysarthric Speech}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={1834--1838},
doi={10.21437/Interspeech.2017-1363}
}