B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://github.com/DeePBluE666/Source-code1.
@article{arxiv.1903.00888,
title = {Denoising convolutional autoencoder based B-mode ultrasound tongue image feature extraction},
author = {Bo Li and Kele Xu and Dawei Feng and Haibo Mi and Huaimin Wang and Jian Zhu},
journal= {arXiv preprint arXiv:1903.00888},
year = {2019}
}