Automatic generation of phonetic regression class trees for MLLR adaptation. - Haeb-Umbach, R.
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
Keywords: MLLR adaptation; Acoustic space; Adaptation experiments; Automatic generation; Bottom-up clustering; Broad phonetic class regression trees; Correlation criterion; Maximum likelihood linear regression based speaker adaptation; Phonetic regression class trees; Speaker-independent training data; Speech units