{"title":"语音信号自动识别系统对语音信号退化因素的鲁棒性分析","authors":"J. Oska, J. Wojtun, K. Wodecki, Z. Piotrowski","doi":"10.1109/SPA.2015.7365136","DOIUrl":null,"url":null,"abstract":"In the article there are presented the results of research on the influence of the lossy compression, used in codecs G.711, G.723.1 and iLBC, on the efficiency of isolated speech phrase recognition. In the research the degree of robustness against degrading factors in the parameterisation method of audio signal LPCC and MFCC (Linear Prediction Cepstral Coefficients, Mel Frequency Cepstral Coefficients) is compared. The research is based on the classifier of improved Gaussian mixtures making allowance for Universal Background Model GMM-UBM (Gaussian Mixtures Model - Universal Background Model). The research was conducted on the database composed of 3000 isolated speech phrases.","PeriodicalId":423880,"journal":{"name":"2015 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robustness analysis of automatic speech signal recognition system against factors degrading speech signal\",\"authors\":\"J. Oska, J. Wojtun, K. Wodecki, Z. Piotrowski\",\"doi\":\"10.1109/SPA.2015.7365136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the article there are presented the results of research on the influence of the lossy compression, used in codecs G.711, G.723.1 and iLBC, on the efficiency of isolated speech phrase recognition. In the research the degree of robustness against degrading factors in the parameterisation method of audio signal LPCC and MFCC (Linear Prediction Cepstral Coefficients, Mel Frequency Cepstral Coefficients) is compared. The research is based on the classifier of improved Gaussian mixtures making allowance for Universal Background Model GMM-UBM (Gaussian Mixtures Model - Universal Background Model). The research was conducted on the database composed of 3000 isolated speech phrases.\",\"PeriodicalId\":423880,\"journal\":{\"name\":\"2015 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPA.2015.7365136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPA.2015.7365136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robustness analysis of automatic speech signal recognition system against factors degrading speech signal
In the article there are presented the results of research on the influence of the lossy compression, used in codecs G.711, G.723.1 and iLBC, on the efficiency of isolated speech phrase recognition. In the research the degree of robustness against degrading factors in the parameterisation method of audio signal LPCC and MFCC (Linear Prediction Cepstral Coefficients, Mel Frequency Cepstral Coefficients) is compared. The research is based on the classifier of improved Gaussian mixtures making allowance for Universal Background Model GMM-UBM (Gaussian Mixtures Model - Universal Background Model). The research was conducted on the database composed of 3000 isolated speech phrases.