Please use this identifier to cite or link to this item:
Title: Visual isolated word recognition
Authors: Foo, Say Wei
Lim, Chee Chuan
Issue Date: 2009
Source: Foo, S. W., & Lim, C. C. Visual isolated word reconition. International Conference on Information, Communications & Signal Processing (pp.1-4).
Abstract: In this paper, the performance of neural network for isolated word recognition based on the movement of the mouth is investigated. Ten isolated words "zero" to "nine" form the vocabulary. The three parameters: height between the upper and lower lips, width of the mouth from left edge to right edge and the specific angle of the upper lip, are selected as representative features of the shape of the mouth. A oneagainst-the-rest neural network is used for classification. The architecture of the neural network consists of ten sub-units. Each sub-unit distinguishes 1 word from the other 9 and has 50 input vectors, 10 hidden nodes and 1 output neuron. By suitably combining the outcomes from the 3 features individually, 92.5% accuracy of recognition may be attained.
Rights: © IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

Files in This Item:
File Description SizeFormat 
C54-P0256-ICICS2001LimCC.pdfAccepted version143.34 kBAdobe PDFThumbnail

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.