Technical Program

Paper Detail

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.