Paper Info
Title
Semantic Edge Detection For Tracking Vocal Tract Air-Tissue Boundaries In Real-Time Magnetic Resonance Images
Abstract
Recent developments in real-time magnetic resonance imaging (rtMRI) have enabled the study of vocal tract dynamics during production of running speech at high frame rates (e.g., 83 frames per second). Such large amounts of acquired data require scalable automated methods to identify different articulators (e.g., tongue, velum) for further analysis. In this paper, we propose a convolutional neural network with an encoder decoder architecture to jointly detect the relevant air-tissue boundaries as well as to label them, which we refer to as 'semantic edge detection'. We pose this as a pixel labeling problem, with the outline contour of each articulator of interest as positive class and the remaining tissue and airway as negative classes. We introduce a loss function modified with additional penalty for misclassification at air-tissue boundaries to account for class imbalance and improve edge localization. We then use a greedy search algorithm to draw contours from the probability maps of the positive classes predicted by the network. The articulator contours obtained by our method are comparable to the true labels generated by iteratively fitting a manually created subject-specific template. Our results generalize well across subjects and different vocal tract postures, demonstrating a significant improvement over the structured regression baseline.
Year
DOI
Venue
2017
10.21437/Interspeech.2017-1580
18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION
Keywords
Field
DocType
Real-time magnetic resonance imaging (MRI), vocal-tract dynamics, contour drawing, convolutional neural networks (CNN)
Computer vision,Computer science,Edge detection,Speech recognition,Artificial intelligence,Vocal tract,Magnetic resonance imaging
Conference
ISSN
Citations 
PageRank 
2308-457X
0
0.34
References 
Authors
7
3
Name
Order
Citations
PageRank
Krishna S.198.31
Asterios Toutios2256.55
Narayanan Shrikanth35558439.23