{"title":"基于MRI数据的三维线性关节模型","authors":"P. Badin, G. Bailly, M. Raybaudi, C. Segebarth","doi":"10.21437/ICSLP.1998-353","DOIUrl":null,"url":null,"abstract":"Based on a set of 3D vocal tract images obtained by MRI, a 3D statistical articulatory model has been built using guided Principal Component Analysis. It constitutes an extension to the lateral dimension of the mid-sagittal model previously developed from a radiofilm recorded on the same subject. The parameters of the 2D model have been found to be good predictors of the 3D shapes, for most configurations. A first evaluation of the model in terms of area functions and formants is presented.","PeriodicalId":90685,"journal":{"name":"Proceedings : ICSLP. International Conference on Spoken Language Processing","volume":"32 1","pages":"249-254"},"PeriodicalIF":0.0000,"publicationDate":"1998-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"69","resultStr":"{\"title\":\"A three-dimensional linear articulatory model based on MRI data\",\"authors\":\"P. Badin, G. Bailly, M. Raybaudi, C. Segebarth\",\"doi\":\"10.21437/ICSLP.1998-353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on a set of 3D vocal tract images obtained by MRI, a 3D statistical articulatory model has been built using guided Principal Component Analysis. It constitutes an extension to the lateral dimension of the mid-sagittal model previously developed from a radiofilm recorded on the same subject. The parameters of the 2D model have been found to be good predictors of the 3D shapes, for most configurations. A first evaluation of the model in terms of area functions and formants is presented.\",\"PeriodicalId\":90685,\"journal\":{\"name\":\"Proceedings : ICSLP. International Conference on Spoken Language Processing\",\"volume\":\"32 1\",\"pages\":\"249-254\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"69\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings : ICSLP. International Conference on Spoken Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21437/ICSLP.1998-353\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings : ICSLP. International Conference on Spoken Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/ICSLP.1998-353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A three-dimensional linear articulatory model based on MRI data
Based on a set of 3D vocal tract images obtained by MRI, a 3D statistical articulatory model has been built using guided Principal Component Analysis. It constitutes an extension to the lateral dimension of the mid-sagittal model previously developed from a radiofilm recorded on the same subject. The parameters of the 2D model have been found to be good predictors of the 3D shapes, for most configurations. A first evaluation of the model in terms of area functions and formants is presented.
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