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
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Topic: |
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Paper Title: |
SENTENCE RECOGNITION FROM ARTICULATORY MOVEMENTS FOR SILENT SPEECH INTERFACES
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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. |