{"title":"Voice Transformation Using Two-Level Dynamic Warping","authors":"Al-Waled Al-Dulaimi, T. Moon, J. Gunther","doi":"10.1109/IEEECONF44664.2019.9048717","DOIUrl":null,"url":null,"abstract":"Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping. An outer warping process, which temporally aligns blocks of speech (dynamic time warp), invokes an inner warping process, which spectrally aligns based on magnitude spectra (dynamic frequency warp). The mapping function produced by the dynamic frequency warp is used to move spectral information from a source speaker to a target speaker. Information obtained by this process is used to train an artificial neural network to produce spectral warping output information based on spectral input data.","PeriodicalId":6684,"journal":{"name":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","volume":"1 1","pages":"143-147"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 53rd Asilomar Conference on Signals, Systems, and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEECONF44664.2019.9048717","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping. An outer warping process, which temporally aligns blocks of speech (dynamic time warp), invokes an inner warping process, which spectrally aligns based on magnitude spectra (dynamic frequency warp). The mapping function produced by the dynamic frequency warp is used to move spectral information from a source speaker to a target speaker. Information obtained by this process is used to train an artificial neural network to produce spectral warping output information based on spectral input data.