{"title":"基于mel-frequency倒谱系数编码的单音素识别神经网络","authors":"Dino Kosic","doi":"10.1109/NEUREL.2010.5644071","DOIUrl":null,"url":null,"abstract":"This paper proposes novel approach in coding single phonemes based on mel-frequency cepstral coefficients (MFCC) in order to simplify the neural network used to recognize those phonemes. The efficiency and effectiveness of proposed algorithm are demonstrated for both male and female speakers.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Neural network for single phoneme recognition based on mel-frequency cepstral coefficients coding\",\"authors\":\"Dino Kosic\",\"doi\":\"10.1109/NEUREL.2010.5644071\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes novel approach in coding single phonemes based on mel-frequency cepstral coefficients (MFCC) in order to simplify the neural network used to recognize those phonemes. The efficiency and effectiveness of proposed algorithm are demonstrated for both male and female speakers.\",\"PeriodicalId\":227890,\"journal\":{\"name\":\"10th Symposium on Neural Network Applications in Electrical Engineering\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"10th Symposium on Neural Network Applications in Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2010.5644071\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"10th Symposium on Neural Network Applications in Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2010.5644071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural network for single phoneme recognition based on mel-frequency cepstral coefficients coding
This paper proposes novel approach in coding single phonemes based on mel-frequency cepstral coefficients (MFCC) in order to simplify the neural network used to recognize those phonemes. The efficiency and effectiveness of proposed algorithm are demonstrated for both male and female speakers.