- Special Session 1: Intelligent Technologies for Spoken Language Learning, Assessment, and Therapy
- Jinsong Zhang
- Beijing Language and Culture University, P. R. China
- Nancy F. Chen
- Institute for Infocomm Research, Singapore
While many recent developments in deep learning has advanced spoken language technology significantly,
intelligent technologies to aid language learning, assess spoken language abilities,
and speech and hearing therapy are still in great demand.
In addition, most of such human language technology applications have been developed in English with abundant training data.
For other spoken languages with less training data, more technical challenges need to be addressed.
This special session attempts to provide an opportunity for researchers to discuss such issues.
- Special Session 2: Advanced Computational Modeling to Improve Human and Machine Communication
- Hung-yi Lee
- Department of Electrical Engineering of National Taiwan University, Taiwan
- Ying-Hui Lai
- Department of Biomedical Engineering, National Yang-Ming University, Taiwan
- Yu Tsao
- Research Center for Information Technology Innovation (CITI) at Academia Sinica, Taiwan
- Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
Human and machine communication can be conveyed via multiple modalities, such as audio, speech, text, body language (e.g., facial expressions or gestures), and tactile signals.
In real-world scenarios, the performance of such interactions might be degraded due to physical limitations caused by humans or the surrounding environment.
Identifying effective ways to improve such communication under undersirable conditions is an important research topic.
In the past, many algorithms have been developed while the issue has not been perfectly addressed.
This special session aims to invite submissions that discuss novel techniques,
which are derived based on advanced signal processing, deep learning algorithms, reinforcement learning, generative adversarial networks,
and brain- and cognition-inspired computing, to improve the human-human and human-machine communication.