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dc.contributor.authorShannon, Matten
dc.contributor.authorZen, Heigaen
dc.contributor.authorByrne, Williamen
dc.date.accessioned2013-04-10T10:52:16Z
dc.date.available2013-04-10T10:52:16Z
dc.date.issued2013-03en
dc.identifier.citationM. Shannon, H. Zen, and W. Byrne, "Autoregressive models for statistical parametric speech synthesis," IEEE Trans. Audio Speech Language Process., vol. 21, no. 3, pp. 587-597, 2013.en_GB
dc.identifier.issn1558-7916
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/244407
dc.description.abstractWe propose using the autoregressive hidden Markov model (HMM) for speech synthesis. The autoregressive HMM uses the same model for parameter estimation and synthesis in a consistent way, in contrast to the standard approach to statistical parametric speech synthesis. It supports easy and efficient parameter estimation using expectation maximization, in contrast to the trajectory HMM. At the same time its similarities to the standard approach allow use of established high quality synthesis algorithms such as speech parameter generation considering global variance. The autoregressive HMM also supports a speech parameter generation algorithm not available for the standard approach or the trajectory HMM and which has particular advantages in the domain of real-time, low latency synthesis. We show how to do efficient parameter estimation and synthesis with the autoregressive HMM and look at some of the similarities and differences between the standard approach, the trajectory HMM and the autoregressive HMM. We compare the three approaches in subjective and objective evaluations. We also systematically investigate which choices of parameters such as autoregressive order and number of states are optimal for the autoregressive HMM.
dc.description.sponsorshipThis work was supported in part by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 213845 (EMIME) and in part by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology).
dc.languageen_USen
dc.language.isoen_USen_GB
dc.publisherIEEE (Institute of Electrical and Electronics Engineers)
dc.subjectacoustic modelingen
dc.subjectautoregressive hidden Markov modelen
dc.subjectautoregressive processesen
dc.subjecthidden Markov models (HMMs)en
dc.subjectspeechen
dc.subjectstatistical parametric speech synthesisen
dc.titleAutoregressive models for statistical parametric speech synthesisen
dc.typeArticle
dc.description.versionCopyright 2013 IEEE.en
prism.publicationDate2013en
rioxxterms.versionofrecord10.1109/TASL.2012.2227740en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2013-03en
dc.identifier.eissn1558-7924
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEC FP7 CP (213845)
dc.identifier.urlhttp://mi.eng.cam.ac.uk/~sms46/papers/shannon2013autoregressive.pdfen


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