Journal article
A new multi-purpose audio-visual UNMC-VIER database with multiple variabilities
Pattern Recognition Letters, Vol.32(13), pp.1503-1510
2011
Abstract
Audio-visual recognition system is becoming popular because it overcomes certain problems of traditional audio-only recognition system. However, difficulties due to visual variations in video sequence can significantly degrade the recognition performance of the system. This problem can be further complicated when more than one visual variation happen at the same time. Although several databases have been created in this area, none of them includes realistic visual variations in video sequence. With the aim to facilitate the development of robust audio-visual recognition systems, the new audio-visual UNMC-VIER database is created. This database contains various visual variations including illumination, facial expression, head pose, and image resolution variations. The most unique aspect of this database is that it includes more than one visual variation in the same video recording. For the audio part, the utterances are spoken in slow and normal speech pace to improve the learning process of audio-visual speech recognition system. Hence, this database is useful for the development of robust audio-visual person, speech recognition and face recognition systems.
Details
- Title
- A new multi-purpose audio-visual UNMC-VIER database with multiple variabilities
- Authors
- Yee Wan Wong (Author) - University of Nottingham Malaysia CampusSue Inn Ch'ng (Author) - University of Nottingham Malaysia CampusKah Phooi Seng (Author) - University of Nottingham Malaysia CampusLi-Minn Ang (Author) - University of Nottingham Malaysia CampusSiew Wen Chin (Author) - University of Nottingham Malaysia CampusWei Jen Chew (Author) - University of Nottingham Malaysia CampusKing Hann Lim (Author) - University of Nottingham Malaysia Campus
- Publication details
- Pattern Recognition Letters, Vol.32(13), pp.1503-1510
- Publisher
- Elsevier BV - North-Holland
- Date published
- 2011
- DOI
- 10.1016/j.patrec.2011.06.011
- ISSN
- 0167-8655; 1872-7344; 0167-8655
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99513895302621
- Output Type
- Journal article
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- Web Of Science research areas
- Computer Science, Artificial Intelligence