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VISUAL SPEECH RECOGNITION FOR ISOLATED DIGITS USING DISCRETE COSINE TRANSFORM AND LOCAL BINARY PATTERN FEATURES

By Abhilash Jain and GN Rathna

Abstract

Visual Speech Recognition (VSR) deals with the task of extracting speech information from visual cues from a person's face while speaking. Accurate lip segmentation and modeling are essential in any VSR algorithm for good feature extraction. However, lip modeling is a complicated task and is not very robust in natural conditions. This paper describes a novel technique for limited vocabulary visual-only speech recognition that does not use lip modeling. For visual feature extraction, Discrete Cosine Transform (DCT) and Local Binary Pattern (LBP) have been tested. An Error-Correcting Output Codes (ECOC) multi-class model using Support Vector Machine (SVM) binary learners is used for recognition and classification of words

Topics: Electrical Engineering
Publisher: IEEE
Year: 2017
DOI identifier: 10.1109/GlobalSIP.2017.8308666
OAI identifier: oai:eprints.iisc.ac.in:61270
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