LATTICE-BASED UNSUPERVISED MLLR FOR SPEAKER ADAPTATION
Mukund Padmanabhan, George Saon, Geoffrey Zweig · 2000
In this paper we explore the use of lattice-based information for unsupervised speaker adaptation. As initially formulated, maximum likelihood linear regression (MLLR) aims to linearly transform the means of the gaussian models in order to maximize the likelihood of the adaptation data given the correct hypothesis (supervised MLLR) or the decoded hypothesis (unsupervised MLLR). For the latter, if the first-pass decoded hypothesis is extremely erroneous (as it is the case for large vocabulary telephony applications) MLLR will often find a transform that increases the likelihood for the incorrect models, and may even lower the likelihood of the correct hypothesis. Since the oracle word error rate of a lattice is much lower than that of the 1-best or N-best hypotheses, by performing adaptation against a word lattice, the correct models are more likely to be used in estimating the transform. Furthermore, the particular MAP lattice that we propose enables the use of a natural confidence mea...