{"title":"HMM语音识别算法编码","authors":"S. Jarng","doi":"10.1109/ICISA.2011.5772321","DOIUrl":null,"url":null,"abstract":"In this paper, the voice recognition algorithm based on HMM (Hidden Markov Modeling) is analyzed in detail. The HMM voice recognition algorithm is explained and the importance of voice information DB is revealed for better improvement of voice recognition rate. The feature vector of each voice characteristic parameter is chosen by means of MFCC (Mel Frequency Cepstral Coefficients). The extracting algorithm of syllable parts from continuous voice signal is introduced. This paper shows the relationship between recognition rates and number of applying syllables and number of groups for applying syllables.","PeriodicalId":425210,"journal":{"name":"2011 International Conference on Information Science and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"HMM Voice Recognition Algorithm Coding\",\"authors\":\"S. Jarng\",\"doi\":\"10.1109/ICISA.2011.5772321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the voice recognition algorithm based on HMM (Hidden Markov Modeling) is analyzed in detail. The HMM voice recognition algorithm is explained and the importance of voice information DB is revealed for better improvement of voice recognition rate. The feature vector of each voice characteristic parameter is chosen by means of MFCC (Mel Frequency Cepstral Coefficients). The extracting algorithm of syllable parts from continuous voice signal is introduced. This paper shows the relationship between recognition rates and number of applying syllables and number of groups for applying syllables.\",\"PeriodicalId\":425210,\"journal\":{\"name\":\"2011 International Conference on Information Science and Applications\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Information Science and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISA.2011.5772321\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Information Science and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISA.2011.5772321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, the voice recognition algorithm based on HMM (Hidden Markov Modeling) is analyzed in detail. The HMM voice recognition algorithm is explained and the importance of voice information DB is revealed for better improvement of voice recognition rate. The feature vector of each voice characteristic parameter is chosen by means of MFCC (Mel Frequency Cepstral Coefficients). The extracting algorithm of syllable parts from continuous voice signal is introduced. This paper shows the relationship between recognition rates and number of applying syllables and number of groups for applying syllables.
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