Visual speech recognition for isolated digits using discrete cosine transform and local binary pattern features

Jain, Abhilash and Rathna, GN (2018) Visual speech recognition for isolated digits using discrete cosine transform and local binary pattern features. In: 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017, 14-16 November 2017, Montreal, 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
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Discrete cosine transform; Feature extraction; Local binary patterns; Visual speech recognition
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	07 Jun 2022 10:27
Last Modified:	07 Jun 2022 10:27
URI:	https://eprints.iisc.ac.in/id/eprint/73206

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