Hebbar S.A.Sharma R.Somandepalli K.Toutios A.Narayanan S.2021-05-052021-05-052020ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings , Vol. 2020-May , , p. 7354 - 7358https://doi.org/10.1109/ICASSP40776.2020.9053111https://idr.nitk.ac.in/handle/123456789/15118Due 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.Conference Paper