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Visual Lip Reading using 3D-DCT and 3D-DWT and LSDA

by Sunil S. Morade, Suprava Patnaik
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 4
Year of Publication: 2016
Authors: Sunil S. Morade, Suprava Patnaik

Sunil S. Morade, Suprava Patnaik . Visual Lip Reading using 3D-DCT and 3D-DWT and LSDA. International Journal of Computer Applications. 136, 4 ( February 2016), 7-15. DOI=10.5120/ijca2016908308

@article{ 10.5120/ijca2016908308,
author = { Sunil S. Morade, Suprava Patnaik },
title = { Visual Lip Reading using 3D-DCT and 3D-DWT and LSDA },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 4 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 7-15 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016908308 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:36:06.430964+05:30
%A Sunil S. Morade
%A Suprava Patnaik
%T Visual Lip Reading using 3D-DCT and 3D-DWT and LSDA
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 4
%P 7-15
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Human uses visual information while trying to understand speech, especially in noisy conditions or in situations where the audio signal is not available. Lip reading is the technique of a comprehensive understanding the underlying speech by processing on the movement of lips. However, the recognition of lip motion is a difficult task since the region of interest (ROI) is nonlinear and noisy. In proposed method lip reading system we have used two stage feature extraction model which is precised, discriminative and computation efficient. The first stage 3D Discrete Wavelet Transform (3D-DWT) or 3D Discrete Cosine Transform (3D-DCT) is used and the second stage is Locality Sensitive Discriminant Analysis (LSDA) to trim down the feature dimensions. These features make a novel lip reading system with small feature vector size. In addition to the novel feature extraction technique, the performance of Naive Bayes and SVM classifier is compared. CUAVE database of 0 to 9 utterances in English is used for experimentation. Results of 3 dimension transform with LSDA are compared with 2 dimension transform with LSDA. Experimental results show that 3D-DWT+LSDA feature mining are compared with 3D-DWT with PCA or LDA. 3D-DWT+LSDA result is also compared with 3D-DCT + LSDA.

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Index Terms

Computer Science
Information Sciences


LSDA LDA 3D-DWT 3D-DCT SVM Naive Bayes Lip reading.