{"title":"利用机器学习创建日语唇读音节数据集的建议","authors":"Rui Kitahara, Lifeng Zhang","doi":"10.12792/ICIAE2021.004","DOIUrl":null,"url":null,"abstract":"Although lip-reading using image processing and machine learning has been mainly performed at the word level, it has been shown that using LipNet, a network that enables recognition at the sentence level, improves the recognition accuracy over the former method. However, this was the case for English speakers. In this study, the data set was created based on speech scenes containing all 50 Japanese sounds, and the recognition accuracy was evaluated using LipNet.","PeriodicalId":161085,"journal":{"name":"The Proceedings of The 9th IIAE International Conference on Industrial Application Engineering 2020","volume":"127 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Proposal for Creating Syllabic Datasets for Japanese Language Lipreading by Using Machine Learning\",\"authors\":\"Rui Kitahara, Lifeng Zhang\",\"doi\":\"10.12792/ICIAE2021.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although lip-reading using image processing and machine learning has been mainly performed at the word level, it has been shown that using LipNet, a network that enables recognition at the sentence level, improves the recognition accuracy over the former method. However, this was the case for English speakers. In this study, the data set was created based on speech scenes containing all 50 Japanese sounds, and the recognition accuracy was evaluated using LipNet.\",\"PeriodicalId\":161085,\"journal\":{\"name\":\"The Proceedings of The 9th IIAE International Conference on Industrial Application Engineering 2020\",\"volume\":\"127 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Proceedings of The 9th IIAE International Conference on Industrial Application Engineering 2020\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12792/ICIAE2021.004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Proceedings of The 9th IIAE International Conference on Industrial Application Engineering 2020","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12792/ICIAE2021.004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Proposal for Creating Syllabic Datasets for Japanese Language Lipreading by Using Machine Learning
Although lip-reading using image processing and machine learning has been mainly performed at the word level, it has been shown that using LipNet, a network that enables recognition at the sentence level, improves the recognition accuracy over the former method. However, this was the case for English speakers. In this study, the data set was created based on speech scenes containing all 50 Japanese sounds, and the recognition accuracy was evaluated using LipNet.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
