Predicting Tongue Motion in Unlabeled Ultrasound Videos Using Convolutional Lstm Neural Networks
- Resource Type
- Conference
- Authors
- Zhao, Chaojie; Zhang, Peng; Zhu, Jian; Wu, Chengrui; Wang, Huaimin; Xu, Kele
- Source
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2019 - 2019 IEEE International Conference on. :5926-5930 May, 2019
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
convolutional recurrent neural network
motion prediction
speech production
ultrasound tongue imaging
silent speech interface
- Language
- ISSN
- 2379-190X
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 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.