{"title":"神经语音预测器","authors":"R. de Figueiredo, E. Akay","doi":"10.1109/ECCSC.2008.4611645","DOIUrl":null,"url":null,"abstract":"In this paper we propose a new neural network architecture that deploys and extended Kalman filter (EKF) based learning algorithm. We used the new neural network for the prediction of speech signals. Simulation results show that the neural networks leads to better performance than the well known linear predictor coefficients (LPC) that uses Levinson-Durbin algorithm.","PeriodicalId":249205,"journal":{"name":"2008 4th European Conference on Circuits and Systems for Communications","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural speech predictors\",\"authors\":\"R. de Figueiredo, E. Akay\",\"doi\":\"10.1109/ECCSC.2008.4611645\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a new neural network architecture that deploys and extended Kalman filter (EKF) based learning algorithm. We used the new neural network for the prediction of speech signals. Simulation results show that the neural networks leads to better performance than the well known linear predictor coefficients (LPC) that uses Levinson-Durbin algorithm.\",\"PeriodicalId\":249205,\"journal\":{\"name\":\"2008 4th European Conference on Circuits and Systems for Communications\",\"volume\":\"47 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 4th European Conference on Circuits and Systems for Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECCSC.2008.4611645\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 4th European Conference on Circuits and Systems for Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECCSC.2008.4611645","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we propose a new neural network architecture that deploys and extended Kalman filter (EKF) based learning algorithm. We used the new neural network for the prediction of speech signals. Simulation results show that the neural networks leads to better performance than the well known linear predictor coefficients (LPC) that uses Levinson-Durbin algorithm.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
