Inter-speaker speech variability assessment using statistical deformable models from 3.0 Tesla magnetic resonance images

• Type: Article in International Scientific Journal
• Date: 2012

Authors

Vasconcelos, MJM (Author) Other; Ventura, SMR (Author) Other; Diamantino Freitas (Author) FEUP; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine

Vol. 226 No. H3

Pages: 185-196

ISSN: 0954-4119

Publisher: SAGE

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-002-H03

DOI: 10.1177/0954411911431664

Abstract (EN): The morphological and dynamic characterisation of the vocal tract during speech production has been gaining greater attention due to the motivation of the latest improvements in magnetic resonance (MR) imaging; namely, with the use of higher magnetic fields, such as 3.0 Tesla. In this work, the automatic study of the vocal tract from 3.0 Tesla MR images was assessed through the application of statistical deformable models. Therefore, the primary goal focused on the analysis of the shape of the vocal tract during the articulation of European Portuguese sounds, followed by the evaluation of the results concerning the automatic segmentation, i.e. identification of the vocal tract in new MR images. In what concerns speech production, this is the first attempt to automatically characterise and reconstruct the vocal tract shape of 3.0 Tesla MR images by using deformable models; particularly, by using active and appearance shape models. The achieved results clearly evidence the adequacy and advantage of the automatic analysis of the 3.0 Tesla MR images of these deformable models in order to extract the vocal tract shape and assess the involved articulatory movements. These achievements are mostly required, for example, for a better knowledge of speech production, mainly of patients suffering from articulatory disorders, and to build enhanced speech synthesizer models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12