Automatic generation of phonetic regression class trees for MLLR adaptation

R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001) 299–302.

Journal Article | English
Abstract
In this paper, it is shown that a correlation criterion is the appropriate criterion for bottom-up clustering to obtain broad phonetic class regression trees for maximum likelihood linear regression (MLLR)-based speaker adaptation. The correlation structure among speech units is estimated on the speaker-independent training data. In adaptation experiments the tree outperformed a regression tree obtained from clustering according to closeness in acoustic space and achieved results comparable with those of a manually designed broad phonetic class tree
Publishing Year
Journal Title
IEEE Transactions on Speech and Audio Processing
Volume
9
Issue
3
Page
299-302
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Haeb-Umbach R. Automatic generation of phonetic regression class trees for MLLR adaptation. IEEE Transactions on Speech and Audio Processing. 2001;9(3):299-302. doi:10.1109/89.906003
Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees for MLLR adaptation. IEEE Transactions on Speech and Audio Processing, 9(3), 299–302. https://doi.org/10.1109/89.906003
@article{Haeb-Umbach_2001, title={Automatic generation of phonetic regression class trees for MLLR adaptation}, volume={9}, DOI={10.1109/89.906003}, number={3}, journal={IEEE Transactions on Speech and Audio Processing}, author={Haeb-Umbach, Reinhold}, year={2001}, pages={299–302} }
Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees for MLLR Adaptation.” IEEE Transactions on Speech and Audio Processing 9, no. 3 (2001): 299–302. https://doi.org/10.1109/89.906003.
R. Haeb-Umbach, “Automatic generation of phonetic regression class trees for MLLR adaptation,” IEEE Transactions on Speech and Audio Processing, vol. 9, no. 3, pp. 299–302, 2001.
Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees for MLLR Adaptation.” IEEE Transactions on Speech and Audio Processing, vol. 9, no. 3, 2001, pp. 299–302, doi:10.1109/89.906003.

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