In this paper a method is presented to estimate a broad phonetic class regression tree to be used in MLLR adap- tation. The tree is derived from the correlation structure among phone units estimated on the training data. The al- gorithm is language-independent and showed good results on both an English and a Mandarin Chinese database. In adaptation experiments the tree outperformed a regres- sion tree obtained from clustering according to closeness in acoustic space and achieved results comparable with those of a manually designed broad phonetic class tree.
Cite as: Haeb-Umbach, R. (2000) Data-driven phonetic regression class tree estimation for MLLR adaptation. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 3, 857-860, doi: 10.21437/ICSLP.2000-669
@inproceedings{haebumbach00_icslp,
author={Reinhold Haeb-Umbach},
title={{Data-driven phonetic regression class tree estimation for MLLR adaptation}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 3, 857-860},
doi={10.21437/ICSLP.2000-669}
}