| Paper: |
SLP-L2.5 |
| Session: |
Spoken and Multimodal Dialog Systems and Applications |
| Location: |
Room B-2 |
| Session Time: |
Thursday, March 29, 14:00 - 16:00 |
| Presentation Time: |
Thursday, March 29, 15:20 - 15:40 |
| Presentation: |
Lecture
|
| Topic: |
|
| Paper Title: |
SENTENCE RECOGNITION FROM ARTICULATORY MOVEMENTS FOR SILENT SPEECH INTERFACES
|
| Authors: |
Jun Wang, Ashok Samal, Jordan R. Green, University of Nebraska-Lincoln, United States; Frank Rudzicz, University of Toronto, Canada |
| Abstract: |
Recent research has demonstrated the potential of using an articulation-based silent speech interface for command-and-control systems. Such an interface converts articulation to words that can then drive a text-to-speech synthesizer. In this paper, we have proposed a novel near-time algorithm to recognize whole-sentences from continuous tongue and lip movements. Our goal is to assist persons who are aphonic or have a severe motor speech impairment to produce functional speech using their tongue and lips. Our algorithm was tested using a functional sentence data set collected from ten speakers (3012 utterances). The average accuracy was 94.89% with an average latency of 3.11 seconds for each sentence prediction. The results indicate the effectiveness of our approach and its potential for building a real-time articulation-based silent speech interface for clinical applications. |