Automatic Evaluation of Speech Intelligibility Based on I-vectors in the Context of Head and Neck Cancers

Imed Laaridh, Corinne Fredouille, Alain Ghio, Muriel Lalain, Virginie Woisard


In disordered speech context and despite its well-known subjectivity, perceptual evaluation is still the most commonly used method in clinical practice to evaluate the intelligibility level of patients' speech productions. However and thanks to increasing computing power, automatic speech processing systems have witnessed a democratization in terms of users and application areas including the medical practice. In this paper, we evaluate an automatic approach for the prediction of cancer patients' speech intelligibility based on the representation of the speech acoustics in the total variability subspace based on the i-vector paradigm. Experimental evaluations of the proposed predictive approach have shown a very high correlation rate with perceptual intelligibility when applied on the French speech corpora C2SI (r=0.84). They have also demonstrated the robustness of the approach when using a limited amount of disordered speech per patient, which may lead to the redesign and alleviation of the test protocols usually used in disordered speech evaluation context.


 DOI: 10.21437/Interspeech.2018-1266

Cite as: Laaridh, I., Fredouille, C., Ghio, A., Lalain, M., Woisard, V. (2018) Automatic Evaluation of Speech Intelligibility Based on I-vectors in the Context of Head and Neck Cancers. Proc. Interspeech 2018, 2943-2947, DOI: 10.21437/Interspeech.2018-1266.


@inproceedings{Laaridh2018,
  author={Imed Laaridh and Corinne Fredouille and Alain Ghio and Muriel Lalain and Virginie Woisard},
  title={Automatic Evaluation of Speech Intelligibility Based on I-vectors in the Context of Head and Neck Cancers},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={2943--2947},
  doi={10.21437/Interspeech.2018-1266},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1266}
}