Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/32078
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features
Author(s): Zhang, Xuejie
Xu, Yan
Abel, Andrew K
Smith, Leslie S
Watt, Roger
Hussain, Amir
Gao, Chengxiang
Keywords: Speech Recognition
Image Processing
Gabor Features
Lip Reading
Explainable
Issue Date: Dec-2020
Date Deposited: 11-Dec-2020
Citation: Zhang X, Xu Y, Abel AK, Smith LS, Watt R, Hussain A & Gao C (2020) Visual Speech Recognition with Lightweight Psychologically Motivated Gabor Features. Entropy, 22 (12), Art. No.: 1367. https://doi.org/10.3390/e22121367
Abstract: Extraction of relevant lip features is of continuing interest in the visual speech domain. 1 Using end-to-end feature extraction can produce good results, but at the cost of the results being 2 difficult for humans to comprehend and relate to. We present a new, lightweight feature extraction 3 approach, motivated by human-centric glimpse based psychological research into facial barcodes, 4 and demonstrate that these simple, easy to extract 3D geometric features (produced using Gabor 5 based image patches), can successfully be used for speech recognition with LSTM based machine 6 learning. This approach can successfully extract low dimensionality lip parameters with a minimum 7 of processing. One key difference between using these Gabor-based features and using other features 8 such as traditional DCT, or the current fashion for CNN features is that these are human-centric 9 features that can be visualised and analysed by humans. This means that it is easier to explain and 10 visualise the results. They can also be used for reliable speech recognition, as demonstrated using the 11 Grid corpus. Results for overlapping speakers using our lightweight system gave a recognition rate 12 of over 82%, which compares well to less explainable features in the literature. 13
DOI Link: 10.3390/e22121367
Rights: Copyright 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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