T
Tobias Bocklet
Researcher at Intel
Publications - 98
Citations - 1506
Tobias Bocklet is an academic researcher from Intel. The author has contributed to research in topics: Speaker recognition & Computer science. The author has an hindex of 19, co-authored 86 publications receiving 1286 citations. Previous affiliations of Tobias Bocklet include University of Erlangen-Nuremberg & SRI International.
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Proceedings ArticleDOI
The INTERSPEECH 2012 Speaker Trait Challenge
Björn Schuller,Stefan Steidl,Anton Batliner,Elmar Nöth,Alessandro Vinciarelli,Felix Burkhardt,Rob J.J.H. van Son,Felix Weninger,Florian Eyben,Tobias Bocklet,Gelareh Mohammadi,Benjamin Weiss +11 more
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.
Proceedings ArticleDOI
Age and gender recognition for telephone applications based on GMM supervectors and support vector machines
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.
Journal ArticleDOI
NeuroSpeech: An open-source software for Parkinson's speech analysis
Juan Rafael Orozco-Arroyave,Juan Camilo Vásquez-Correa,Jesús Francisco Vargas-Bonilla,Raman Arora,Najim Dehak,Phani Sankar Nidadavolu,Heidi Christensen,Frank Rudzicz,Maria Yancheva,Hamidreza Chinaei,Alyssa Vann,Nikolai Vogler,Tobias Bocklet,Milos Cernak,Julius Hannink,Elmar Nöth +15 more
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.
Proceedings ArticleDOI
Detection of persons with Parkinson's disease by acoustic, vocal, and prosodic analysis
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.
Journal ArticleDOI
Towards an automatic evaluation of the dysarthria level of patients with Parkinson's disease.
Juan Camilo Vásquez-Correa,Juan Camilo Vásquez-Correa,Juan Rafael Orozco-Arroyave,Juan Rafael Orozco-Arroyave,Tobias Bocklet,Elmar Nöth +5 more
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.