. Heyning, The dysphonia severity index: an objective measure of vocal quality based on a multiparameter approach, Journal of Speech, Language, and Hearing Research, vol.43, issue.3, pp.796-809, 2000.

A. Lowit and R. D. Kent, Assessment of motor speech disorders, Plural publishing, vol.1, 2010.

S. Fex, Perceptual evaluation, Journal of voice, vol.6, issue.2, pp.155-158, 1992.

H. Christensen, S. Cunningham, C. Fox, P. Green, and T. Hain, A comparative study of adaptive, automatic recognition of disordered speech, Proceedings of Interspeech'12, 2012.

C. Middag, J. Martens, G. Van-nuffelen, and M. D. Bodt, Automated intelligibility assessment of pathological speech using phonological features, EURASIP Journal on Advances in Signal Processing, issue.1, pp.1-9, 2009.

T. Khan, J. Westin, and M. Dougherty, Classification of speech intelligibility in parkinson's disease, Biocybernetics and Biomedical Engineering, vol.34, issue.1, pp.35-45, 2014.

I. Laaridh, C. Fredouille, and C. Meunier, Automatic detection of phone-based anomalies in dysarthric speech, ACM Transactions on accessible computing, vol.6, issue.3, p.24, 2015.

N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, Front-end factor analysis for speaker verification, IEEE Transactions on Audio, Speech, and Language Processing, vol.19, issue.4, pp.788-798, 2011.

P. Verma and P. K. Das, i-vectors in speech processing applications: a survey, International Journal of Speech Technology, vol.18, issue.4, pp.529-546, 2015.

G. An, D. G. Brizan, M. Ma, M. Morales, A. R. Syed et al., Automatic recognition of unified parkinsons disease rating from speech with acoustic, i-vector and phonotactic features, Proceedings of Interspeech'15, 2015.

J. Wang, P. V. Kothalkar, B. Cao, and D. Heitzman, Towards automatic detection of amyotrophic lateral sclerosis from speech acoustic and articulatory samples, Proc. of INTERSPEECH, 2016.

D. Martínez, E. Lleida, P. Green, H. Christensen, A. Ortega et al., Intelligibility assessment and speech recognizer word accuracy rate prediction for dysarthric speakers in a factor analysis subspace, ACM Transactions on Accessible Computing (TACCESS), vol.6, issue.3, p.10, 2015.

N. Garcia, J. R. Orozco-arroyave, L. Dharo, N. Dehak, and E. Nöth, Evaluation of the neurological state of people with parkinsons disease using i-vectors, Proceedings of the 18th INTERSPEECH, 2017.

I. Laaridh, W. Ben-kheder, C. Fredouille, and C. Meunier, Automatic prediction of speech evaluation metrics for dysarthric speech, Proc. Interspeech, pp.1834-1838, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01771613

C. Astesano, M. Balaguer, J. Farinas, C. Fredouille, P. Gaillard et al., Carcinologic Speech Severity Index Project: A Database of Speech Disorders Productions to Assess Quality of Life Related to Speech After Cancer, Language Resources and Evaluation Conference (LREC), Miyazak, Japon, 2018.

D. Matrouf, N. Scheffer, B. G. Fauve, and J. Bonastre, A straightforward and efficient implementation of the factor analysis model for speaker verification, Interspeech, pp.1242-1245, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01318480

M. Ajili, J. Bonastre, W. Ben-kheder, S. Rossato, and J. Kahn, Phonetic content impact on forensic voice comparison, Spoken Language Technology Workshop (SLT), pp.210-217, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02065374

J. Bonastre, N. Scheffer, D. Matrouf, C. Fredouille, A. Larcher et al., Alize/spkdet: a state-of-the-art open source software for speaker recognition, p.20, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01312982

A. Larcher, J. Bonastre, B. G. Fauve, K. Lee, C. Lévy et al., Alize 3.0-open source toolkit for state-of-the-art speaker recognition, pp.2768-2772, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01927586

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and computing, vol.14, issue.3, pp.199-222, 2004.