This paper presents a novel method for modeling phonetic context using linear context transforms. Initial investigations have shown the feasibility of synthesising context dependent models from context independent models through weighted interpolation of the peripheral states of a given hidden markov model with its adjacent model. This idea can be further extended, to maximum likelihood estimation of not only single weights, but a matrix of weights or a transform. This paper outlines the application of Maximum Likelihood Linear Regression (MLLR) as a means of modeling context dependency in continuous density Hidden Markov Models (HMM).
Cite as: Doherty, B., Vaseghi, S., McCourt, P. (1998) Context dependent tree based transforms for phonetic speech recognition. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0323, doi: 10.21437/ICSLP.1998-645
@inproceedings{doherty98_icslp,
author={Bernard Doherty and Saeed Vaseghi and Paul McCourt},
title={{Context dependent tree based transforms for phonetic speech recognition}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0323},
doi={10.21437/ICSLP.1998-645}
}