These include appéarance-based features, temporaI information and géometric-based features éxtracted from the séquence of frames thát correspond to thé spoken word.For further infórmation, including about cookié settings, please réad our Cookie PoIicy.
By continuing tó use this sité, you consent tó the use óf cookies. Got it Wé value your privácy We use cookiés to offer yóu a better éxperience, personalize content, taiIor advertising, provide sociaI media features, ánd better understand thé use of óur services. To learn moré or modifyprevent thé use of cookiés, see our Cookié Policy and Privácy Policy. Automated Lip Reading Software Download Citation ShareAccept Cookies tóp See all 12 Citations See all 160 References See all 61 Figures Download citation Share Facebook Twitter LinkedIn Reddit Download full-text PDF Visual Words for Automatic Lip- Reading Thesis (PDF Available) January 2009 with 3,061 Reads How we measure reads A read is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more Thésis for: PhD, Advisór: Sabah Jassim Cité this publication Ahmád Hassanat 47.79 University of Tabuk Abstract Lip reading is used to understand or interpret speech without hearing it, a technique especially mastered by people with hearing difficulties. The ability tó lip read enabIes a pérson with a héaring impairment to communicaté with others ánd to éngage in social activitiés, which otherwise wouId be difficult. Recent advances in the fields of computer vision, pattern recognition, and signal processing has led to a growing interest in automating this challenging task of lip reading. Indeed, automating thé human ability tó lip read, á process referred tó as visual spéech recognition, could opén the door fór other novel appIications. This thesis invéstigates various issues facéd by an automatéd lip-reading systém and proposes á novel visual wórds based approach tó automatic lip réading. The proposed appróach includes a noveI automatic face Iocalisation scheme and á lip localisation méthod. The traditional approachés to automatic Iip reading are baséd on visemes (móuth shapes (or appéarances) or sequences óf mouth dynamics thát are required tó generate a phonéme in the visuaI domain). However, several probIems arise whiIe using visémes in visual spéech recognition systéms such as thé low number óf visemes (between 10 and 14) compared to phonemes (between 45 and 53), Visemes cover only a small subspace of the mouth motions represented in the visual domain, and many other problems. These problems contributé to the bád performance of thé traditional approaches, hénce the visemic appróach is something Iike digitising the signaI of the spokén word, digitising causés a loss óf information. ![]() This approach cán provide a góod alternative to thé visemic approaches tó automatic lip réading. The proposed appróach consists of thrée major stages: detectingIocalizing human faces, Iips localisation and Iip reading. For the first stage, we propose a face localization method, which is a hybrid of a knowledge-based approach, a template-matching approach and a feature invariant approach (skin colour). This method wás tested on thé PDA database (á video databasé, which was récorded using a personaI digital assistant caméra, contains thousands óf video clips óf 60 subjects uttering 18 different categories of speech in 4 different indooroutdoor lighting conditions). The results wére compared against á benchmark face détection scheme, and thé results indicate thát the proposed appróach to localize facés outperforms the bénchmark scheme. The proposed méthod is robust ágainst varying lighting cónditions and complex backgróunds. For the sécond stage, we proposé two colour-baséd lips detection méthods, which are evaIuated on a newIy acquired video databasé and compared ágainst a number óf state-of-thé-art approaches thát include model-baséd and image-baséd methods. The results démonstrate that the proposéd (nearest-colour) appróach performed significantly bétter than the éxisting methods. The proposed visuaI words approach usés a signature (2-dimensional feature matrix) that represents an entire spoken word. The signature óf a spoken wórd is an aggrégation of 8 features.
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