{"title":"使用瞬时频率和非周期检测来估计高质量语音合成的F0","authors":"Hideki Kawahara, Yannis Agiomyrgiannakis, H. Zen","doi":"10.21437/SSW.2016-36","DOIUrl":null,"url":null,"abstract":"This paper introduces a general and flexible framework for F0 and aperiodicity (additive non periodic component) analysis, specifically intended for high-quality speech synthesis and modification applications. The proposed framework consists of three subsystems: instantaneous frequency estimator and initial aperiodicity detector, F0 trajectory tracker, and F0 refinement and aperiodicity extractor. A preliminary implementation of the proposed framework substantially outperformed (by a factor of 10 in terms of RMS F0 estimation error) existing F0 extractors in tracking ability of temporally varying F0 trajectories. The front end aperiodicity detector consists of a complex-valued wavelet analysis filter with a highly selective temporal and spectral envelope. This front end aperiodicity detector uses a new measure that quantifies the deviation from periodicity. The measure is less sensitive to slow FM and AM and closely correlates with the signal to noise ratio.","PeriodicalId":340820,"journal":{"name":"Speech Synthesis Workshop","volume":"125 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":"{\"title\":\"Using instantaneous frequency and aperiodicity detection to estimate F0 for high-quality speech synthesis\",\"authors\":\"Hideki Kawahara, Yannis Agiomyrgiannakis, H. Zen\",\"doi\":\"10.21437/SSW.2016-36\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a general and flexible framework for F0 and aperiodicity (additive non periodic component) analysis, specifically intended for high-quality speech synthesis and modification applications. The proposed framework consists of three subsystems: instantaneous frequency estimator and initial aperiodicity detector, F0 trajectory tracker, and F0 refinement and aperiodicity extractor. A preliminary implementation of the proposed framework substantially outperformed (by a factor of 10 in terms of RMS F0 estimation error) existing F0 extractors in tracking ability of temporally varying F0 trajectories. The front end aperiodicity detector consists of a complex-valued wavelet analysis filter with a highly selective temporal and spectral envelope. This front end aperiodicity detector uses a new measure that quantifies the deviation from periodicity. The measure is less sensitive to slow FM and AM and closely correlates with the signal to noise ratio.\",\"PeriodicalId\":340820,\"journal\":{\"name\":\"Speech Synthesis Workshop\",\"volume\":\"125 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Speech Synthesis Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21437/SSW.2016-36\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Speech Synthesis Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/SSW.2016-36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using instantaneous frequency and aperiodicity detection to estimate F0 for high-quality speech synthesis
This paper introduces a general and flexible framework for F0 and aperiodicity (additive non periodic component) analysis, specifically intended for high-quality speech synthesis and modification applications. The proposed framework consists of three subsystems: instantaneous frequency estimator and initial aperiodicity detector, F0 trajectory tracker, and F0 refinement and aperiodicity extractor. A preliminary implementation of the proposed framework substantially outperformed (by a factor of 10 in terms of RMS F0 estimation error) existing F0 extractors in tracking ability of temporally varying F0 trajectories. The front end aperiodicity detector consists of a complex-valued wavelet analysis filter with a highly selective temporal and spectral envelope. This front end aperiodicity detector uses a new measure that quantifies the deviation from periodicity. The measure is less sensitive to slow FM and AM and closely correlates with the signal to noise ratio.