Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network
Abstract
A challenge in speech production research is to predict future tongue movements based on a short period of past tongue movements. This study tackles speaker-dependent tongue motion prediction problem in unlabeled ultrasound videos with convolutional long short-term memory (ConvLSTM) networks. The model has been tested on two different ultrasound corpora. ConvLSTM outperforms 3-dimensional convolutional neural network (3DCNN) in predicting the 9\textsuperscript{th} frames based on 8 preceding frames, and also demonstrates good capacity to predict only the tongue contours in future frames. Further tests reveal that ConvLSTM can also learn to predict tongue movements in more distant frames beyond the immediately following frames. Our codes are available at: https://github.com/shuiliwanwu/ConvLstm-ultrasound-videos.
Keywords
Cite
@article{arxiv.1902.06927,
title = {Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network},
author = {Chaojie Zhao and Peng Zhang and Jian Zhu and Chengrui Wu and Huaimin Wang and Kele Xu},
journal= {arXiv preprint arXiv:1902.06927},
year = {2019}
}
Comments
Accepted by ICASSP 2019