{"title":"基于自适应全频带谐波模型的语音分析与合成","authors":"G. Degottex, Y. Stylianou","doi":"10.1109/TASL.2013.2266772","DOIUrl":null,"url":null,"abstract":"Voice models often use frequency limits to split the speech spectrum into two or more voiced/unvoiced frequency bands. However, from the voice production, the amplitude spectrum of the voiced source decreases smoothly without any abrupt frequency limit. Accordingly, multiband models struggle to estimate these limits and, as a consequence, artifacts can degrade the perceived quality. Using a linear frequency basis adapted to the non-stationarities of the speech signal, the Fan Chirp Transformation (FChT) have demonstrated harmonicity at frequencies higher than usually observed from the DFT which motivates a full-band modeling. The previously proposed Adaptive Quasi-Harmonic model (aQHM) offers even more flexibility than the FChT by using a non-linear frequency basis. In the current paper, exploiting the properties of aQHM, we describe a full-band Adaptive Harmonic Model (aHM) along with detailed descriptions of its corresponding algorithms for the estimation of harmonics up to the Nyquist frequency. Formal listening tests show that the speech reconstructed using aHM is nearly indistinguishable from the original speech. Experiments with synthetic signals also show that the proposed aHM globally outperforms previous sinusoidal and harmonic models in terms of precision in estimating the sinusoidal parameters. As a perspective, such a precision is interesting for building higher level models upon the sinusoidal parameters, like spectral envelopes for speech synthesis.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2266772","citationCount":"56","resultStr":"{\"title\":\"Analysis and Synthesis of Speech Using an Adaptive Full-Band Harmonic Model\",\"authors\":\"G. Degottex, Y. Stylianou\",\"doi\":\"10.1109/TASL.2013.2266772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Voice models often use frequency limits to split the speech spectrum into two or more voiced/unvoiced frequency bands. However, from the voice production, the amplitude spectrum of the voiced source decreases smoothly without any abrupt frequency limit. Accordingly, multiband models struggle to estimate these limits and, as a consequence, artifacts can degrade the perceived quality. Using a linear frequency basis adapted to the non-stationarities of the speech signal, the Fan Chirp Transformation (FChT) have demonstrated harmonicity at frequencies higher than usually observed from the DFT which motivates a full-band modeling. The previously proposed Adaptive Quasi-Harmonic model (aQHM) offers even more flexibility than the FChT by using a non-linear frequency basis. In the current paper, exploiting the properties of aQHM, we describe a full-band Adaptive Harmonic Model (aHM) along with detailed descriptions of its corresponding algorithms for the estimation of harmonics up to the Nyquist frequency. Formal listening tests show that the speech reconstructed using aHM is nearly indistinguishable from the original speech. Experiments with synthetic signals also show that the proposed aHM globally outperforms previous sinusoidal and harmonic models in terms of precision in estimating the sinusoidal parameters. As a perspective, such a precision is interesting for building higher level models upon the sinusoidal parameters, like spectral envelopes for speech synthesis.\",\"PeriodicalId\":55014,\"journal\":{\"name\":\"IEEE Transactions on Audio Speech and Language Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/TASL.2013.2266772\",\"citationCount\":\"56\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Audio Speech and Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TASL.2013.2266772\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Audio Speech and Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TASL.2013.2266772","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis and Synthesis of Speech Using an Adaptive Full-Band Harmonic Model
Voice models often use frequency limits to split the speech spectrum into two or more voiced/unvoiced frequency bands. However, from the voice production, the amplitude spectrum of the voiced source decreases smoothly without any abrupt frequency limit. Accordingly, multiband models struggle to estimate these limits and, as a consequence, artifacts can degrade the perceived quality. Using a linear frequency basis adapted to the non-stationarities of the speech signal, the Fan Chirp Transformation (FChT) have demonstrated harmonicity at frequencies higher than usually observed from the DFT which motivates a full-band modeling. The previously proposed Adaptive Quasi-Harmonic model (aQHM) offers even more flexibility than the FChT by using a non-linear frequency basis. In the current paper, exploiting the properties of aQHM, we describe a full-band Adaptive Harmonic Model (aHM) along with detailed descriptions of its corresponding algorithms for the estimation of harmonics up to the Nyquist frequency. Formal listening tests show that the speech reconstructed using aHM is nearly indistinguishable from the original speech. Experiments with synthetic signals also show that the proposed aHM globally outperforms previous sinusoidal and harmonic models in terms of precision in estimating the sinusoidal parameters. As a perspective, such a precision is interesting for building higher level models upon the sinusoidal parameters, like spectral envelopes for speech synthesis.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.