Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/15118
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dc.contributor.authorHebbar S.A.
dc.contributor.authorSharma R.
dc.contributor.authorSomandepalli K.
dc.contributor.authorToutios A.
dc.contributor.authorNarayanan S.
dc.date.accessioned2021-05-05T10:16:29Z-
dc.date.available2021-05-05T10:16:29Z-
dc.date.issued2020
dc.identifier.citationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings , Vol. 2020-May , , p. 7354 - 7358en_US
dc.identifier.urihttps://doi.org/10.1109/ICASSP40776.2020.9053111
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/15118-
dc.description.abstractDue to its ability to visualize and measure the dynamics of vocal tract shaping during speech production, real-time magnetic resonance imaging (rtMRI) has emerged as one of the prominent research tools. The ability to track different articulators such as the tongue, lips, velum, and the pharynx is a crucial step toward automating further scientific and clinical analysis. Recently, various researchers have addressed the problem of detecting articulatory boundaries, but those are primarily limited to static-image based methods. In this work, we propose to use information from temporal dynamics together with the spatial structure to detect the articulatory boundaries in rtMRI videos. We train a convolutional LSTM network to detect and label the articulatory contours. We compare the produced contours against reference labels generated by iteratively fitting a manually created subject-specific template. We observe that the proposed method outperforms solely image-based methods, especially for the difficult-to-track articulators involved in airway constriction formation during speech. © 2020 IEEE.en_US
dc.titleVocal Tract Articulatory Contour Detection in Real-Time Magnetic Resonance Images Using Spatio-Temporal Contexten_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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