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

Jain, Abhilash and Rathna, GN (2017) VISUAL SPEECH RECOGNITION FOR ISOLATED DIGITS USING DISCRETE COSINE TRANSFORM AND LOCAL BINARY PATTERN FEATURES. In: 2017 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2017), NOV 14-16, 2017, Montreal, QC, Canada, pp. 368-372.

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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.

Item Type:	Conference Paper
Series.:	IEEE Global Conference on Signal and Information Processing
Publisher:	IEEE
Additional Information:	5th IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, CANADA, NOV 14-16, 2017
Keywords:	visual speech recognition; local binary patterns; discrete cosine transform; feature extraction
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	15 Jan 2019 14:59
Last Modified:	15 Jan 2019 14:59
URI:	http://eprints.iisc.ac.in/id/eprint/61270

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