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Unsupervised Noisy Environment Adaptation Algorithm Using MLLR and Speaker Selection

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dc.contributor.author Miichi Yamada ja
dc.contributor.author Akira Baba ja
dc.contributor.author Shinichi Yoshizawa ja
dc.contributor.author Yuichiro Mera ja
dc.contributor.author Akinobu Lee ja
dc.contributor.author Hiroshi Saruwatari ja
dc.contributor.author Kiyohiro Shikano ja
dc.date.accessioned 2012-08-22T07:58:42Z
dc.date.available 2012-08-22T07:58:42Z
dc.date.issued 2001-09 ja
dc.identifier.issn 1018-4074 ja
dc.identifier.uri http://hdl.handle.net/10061/7961
dc.description EUROSPEECH2001: the 7th European Conference on Speech Communication and Technology, September 3-7, 2001, Aalborg, Denmark. ja
dc.description.abstract An unsupervised acoustic model adaptation algorithm using MLLR and speaker selection for noisy environments is proposed. The proposed algorithm requires only one arbitrary utterance and environmental noise data. The adaptation procedure is composed of the following four steps. (1) Speaker selection from a large number of database speakers is carried out using GMM speaker models based on one arbitrary utterance. (2) Initial speaker adapted HMM acoustic models are calculated from the HMM sufficient statistics of the selected speakers, where the sufficient HMM statistics are pre-calculated and stored. (3) A small subset of the clean speech database from the selected speakers and the environment noise data are superimposed. (4) MLLR adaptation is carried out using the noise-superimposed speech database from the selected speakers. The proposed algorithm is evaluated in a 20k vocabulary dictation task for newspaper in noisy environments. We attain 85.7% word correct rate in 25dB SNR, which is slightly better than the matched model by the E-M training using noise superimposed whole speech database. The proposed algorithm is also 7% better than the HMM composition algorithm. ja
dc.language.iso en ja
dc.rights Copyright 2001 ISCA ja
dc.title Unsupervised Noisy Environment Adaptation Algorithm Using MLLR and Speaker Selection ja
dc.type.nii Conference Paper ja
dc.textversion Publisher ja
dc.identifier.spage 869 ja
dc.identifier.epage 872 ja


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