Abstract
This paper proposes a unified system for both visual speech recognition and speaker identification. The proposed system can handle image and depth data if they are available. The proposed system consists of four consecutive steps, namely, 3D face pose tracking, mouth region extraction, features computing, and classification using the Support Vector Machine method. The system is experimentally evaluated on three public datasets, namely, MIRACL-VC1, OuluVS, and CUAVE. In one hand, the visual speech recognition module achieves up to 96 % and 79.2 % for speaker dependent and speaker independent settings, respectively. On the other hand, speaker identification performs up to 98.9 % of recognition rate. Additionally, the obtained results demonstrate the importance of the depth data to resolve the subject dependency issue.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
