Automatic tongue contour extraction in ultrasound images with convolutional neural networks
Ultrasound imaging of the tongue can provide detailed articulatory information addressing a variety of phonetic questions. However, using this method often requires the time consuming process of manually labeling tongue contours in noisy images. This study presents a method for the automatic identification and extraction of tongue contours using convolutional neural networks, a machine learning algorithm that has been shown to be highly successful in many biomedical image segmentation tasks. We have adopted the U net architecture (Ronneberger, Fischer, Brox 2015, U Net: Convolutional Networks for Biomedical Image Segmentation, DOI:10.1007/978 3 319 24574 428) which learns from human annotated splines using multiple, repeated convolution and max pooling layers in the network for feature extraction, as well as deconvolution layers for generating spatially precise predictions of the tongue contours. Trained using a preliminary dataset of 8881 human labeled tongue images from three speakers, our model generates discrete tongue splines comparable to those identified by human annotators (Dice Similarity Coefficient 0.71) Although work is ongoing, this neural network based method shows considerable promise for the post processing of ultrasound images in phonetic research. [Work supported by NSF grant BCS 1348150 to P.S. Beddor and A.W. Coetzee.]Ultrasound imaging of the tongue can provide detailed articulatory information addressing a variety of phonetic questions. However, using this method often requires the time consuming process of manually labeling tongue contours in noisy images. This study presents a method for the automatic identification and extraction of tongue contours using convolutional neural networks, a machine learning algorithm that has been shown to be highly successful in many biomedical image segmentation tasks. We have adopted the U net architecture (Ronneberger, Fischer, Brox 2015, U Net: Convolutional Networks for Biomedical Image Segmentation, DOI:10.1007/978 3 319 24574 428) which learns from human annotated splines using multiple, repeated convolution and max pooling layers in the network for feature extraction, as well as deconvolution layers for generating spatially precise predictions of the tongue contours. Trained using a preliminary dataset of 8881 human labeled tongue images from three speakers, our model gene.