@inproceedings{oai:nitech.repo.nii.ac.jp:00003402,
 author = {Shiota, Sayaka and Hashimoto, Kei and Nankaku, Yoshihiko and 南角, 吉彦 and Tokuda, Keiichi},
 book = {INTERSPEECH 2009 10th Annual Conference of the International Speech Communication Association},
 month = {},
 note = {application/pdf, This paper proposes a deterministic annealing based training algorithmfor Bayesian speech recognition. The Bayesian methodis a statistical technique for estimating reliable predictive distributionsby marginalizing model parameters. However, the localmaxima problem in the Bayesian method is more serious thanin the ML-based approach, because the Bayesian method treatsnot only state sequences but also model parameters as latentvariables. The deterministic annealing EM (DAEM) algorithmhas been proposed to improve the local maxima problem in theEM algorithm, and its effectiveness has been reported in HMMbasedspeech recognition using ML criterion. In this paper, theDAEM algorithm is applied to Bayesian speech recognition torelax the local maxima problem. Speech recognition experimentsshow that the proposed method achieved a higher performancethan the conventional methods., Brighton, United KingdomSeptember 6-10, 2009},
 pages = {680--683},
 publisher = {International Speech Communication Association},
 title = {Deterministic Annealing Based Training Algorithm for Bayesian Speech Recognition},
 year = {2009},
 yomi = {ナンカク, ヨシヒコ}
}