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dc.contributor.authorLin, Bor-Shingen_US
dc.contributor.authorYao, Yu-Hsienen_US
dc.contributor.authorLiu, Ching-Fengen_US
dc.contributor.authorLien, Ching-Fengen_US
dc.contributor.authorLin, Bor-Shyhen_US
dc.date.accessioned2019-04-03T06:35:40Z-
dc.date.available2019-04-03T06:35:40Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn2169-3536en_US
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2017.2649838en_US
dc.identifier.urihttp://hdl.handle.net/11536/144819-
dc.description.abstractTotal laryngectomy is a common treatment for patients with advanced laryngeal and hypopharyngeal cancer, but it is also a result from the loss of the natural voice and directly affects the basic communication functions in daily life. Reconstructing the basic communication function is an important issue for these patients after total laryngectomy surgery. Recently, the image processing technique for lip-reading recognition has been widely developed and applied in various kinds of applications. It is also one of the possibly alternative approaches to reconstructing the basic communication function for these patients after total laryngectomy surgery. Although many human lip-reading recognition methods have been developed to detect lip contour precisely, detecting pronouncing lip contour effectively is still a difficult challenge. In this paper, a novel lip-reading recognition algorithm was proposed to recognize English vowels from the lip contour when speaking. Here, several criteria for detecting the mouth region of interest (ROI) were designed to reduce the error rate of detecting the mouth ROI and lip contour. Moreover, several lip parameters, including the width, height, contour points, area, and the ratio (width/height) of lips, were used to recognize the lip contour and English vowels when speaking. The advantages of the proposed method are that it could detect the mouth ROI automatically, reduce the influence of individual differences, such as the individual lip shape or makeup effect, and it also could perform a good performance without pretraining. Finally, the performance of lip-reading recognition under different backgrounds and individual differences was also tested, and the accuracy of the proposed algorithm on lip-reading recognition was over 80%.en_US
dc.language.isoen_USen_US
dc.subjectLaryngectomyen_US
dc.subjectlip-reading recogonitionen_US
dc.subjectmouth region of interesten_US
dc.subjectvisual-only speech recognitionen_US
dc.subjectvowels recognitionen_US
dc.titleDevelopment of Novel Lip-Reading Recognition Algorithmen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2017.2649838en_US
dc.identifier.journalIEEE ACCESSen_US
dc.citation.volume5en_US
dc.citation.spage794en_US
dc.citation.epage801en_US
dc.contributor.department影像與生醫光電研究所zh_TW
dc.contributor.departmentInstitute of Imaging and Biomedical Photonicsen_US
dc.identifier.wosnumberWOS:000397136900008en_US
dc.citation.woscount1en_US
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