Tobias Bocklet

TL;DR: The EPFL-CONF-174360 data indicate that speaker Traits and Likability are influenced by the environment and the speaker’s personality in terms of paralinguistics and personality.

TL;DR: This paper compares two approaches of automatic age and gender classification with 7 classes of Gaussian mixture models with universal background models, which are well known for the task of speaker identification/verification.

TL;DR: This is the first software with the characteristics described above, and it is considered that it will help other researchers to contribute to the state-of-the-art in pathological speech assessment from different perspectives, e.g., from the clinical point of view for interpretation, and from the computer science point of views enabling the test of different measures and pattern recognition techniques.

TL;DR: It is shown that read texts and monologues are the most meaningful texts when it comes to the automatic detection of PD based on articulation, voice, and prosodic evaluations.

TL;DR: The proposed approach may help clinicians to make more accurate and timely decisions about the evaluation and therapy associated to the dysarthria level of patients, and is a great step towards unobtrusive/ecological evaluations of patients with dysarthric speech without the need of attending medical appointments.