@inproceedings{oai:nitech.repo.nii.ac.jp:00003413,
 author = {Takaki, Shinji and Nankaku, Yoshihiko and 南角, 吉彦 and Tokuda, Keiichi and 徳田, 恵一},
 book = {SSW7 The Seventh ISCA Tutorial and Research Workshop (ITRW) on Speech Synthesis},
 month = {},
 note = {application/pdf, This paper proposes a spectral modeling technique based on additivestructure of context dependencies for HMM-based speechsynthesis. Contextual additive structure models can representcomplicated dependencies between acoustic features and contextlabels using multiple decision trees. However, its computationalcomplexity of the context clustering is too high for fullcontext labels of speech synthesis. To overcome this problem,this paper proposes two approaches; covariance parameter tyingand a likelihood calculation algorithm using matrix inversionlemma. Experimental results show that the proposed methodoutperforms the conventional one in subjective listening tests., NICT/ATR, Kyoto, Japan, September 22-24, 2010},
 pages = {100--105},
 publisher = {International Speech Communication Association},
 title = {Spectral Modeling with Contextual Additive Structure for HMM-based Speech Synthesis},
 year = {2010},
 yomi = {ナンカク, ヨシヒコ and トクダ, ケイイチ}
}