{"title":"高阶隐马尔可夫建模","authors":"J. du Preez, D. Weber","doi":"10.1109/COMSIG.1998.736948","DOIUrl":null,"url":null,"abstract":"Recently we introduced algorithms for the efficient processing of high-order hidden Markov models (HMMs) of fixed order into the literature. In this paper these techniques are generalised to also include mixed-order HMMs. This then allows first-order equivalents to be found for all HMMs, thereby providing a unifying base for reasoning about their properties. Specifically we provide formulations that separates the duration modelling and context modelling capabilities of high-order HMMs.","PeriodicalId":294473,"journal":{"name":"Proceedings of the 1998 South African Symposium on Communications and Signal Processing-COMSIG '98 (Cat. No. 98EX214)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"High-order hidden Markov modelling\",\"authors\":\"J. du Preez, D. Weber\",\"doi\":\"10.1109/COMSIG.1998.736948\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently we introduced algorithms for the efficient processing of high-order hidden Markov models (HMMs) of fixed order into the literature. In this paper these techniques are generalised to also include mixed-order HMMs. This then allows first-order equivalents to be found for all HMMs, thereby providing a unifying base for reasoning about their properties. Specifically we provide formulations that separates the duration modelling and context modelling capabilities of high-order HMMs.\",\"PeriodicalId\":294473,\"journal\":{\"name\":\"Proceedings of the 1998 South African Symposium on Communications and Signal Processing-COMSIG '98 (Cat. No. 98EX214)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1998 South African Symposium on Communications and Signal Processing-COMSIG '98 (Cat. No. 98EX214)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMSIG.1998.736948\",\"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 of the 1998 South African Symposium on Communications and Signal Processing-COMSIG '98 (Cat. No. 98EX214)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMSIG.1998.736948","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recently we introduced algorithms for the efficient processing of high-order hidden Markov models (HMMs) of fixed order into the literature. In this paper these techniques are generalised to also include mixed-order HMMs. This then allows first-order equivalents to be found for all HMMs, thereby providing a unifying base for reasoning about their properties. Specifically we provide formulations that separates the duration modelling and context modelling capabilities of high-order HMMs.
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