Joint maximum a posteriori estimation of transformation and hidden Markov model parameters
Olivier Siohan, Cristina Chesta, Chin‐Hui Lee · 2002
Model adaptation techniques can usually be divided into indirect and direct approaches. On one hand, indirect or transformation-based techniques assume that a general transformation shared amongst different acoustic units is applied to clusters of model parameters. Such approaches (e.g. MLLR-maximum likelihood linear regression) are quite efficient when the amount of adaptation data is limited, but have poor asymptotic properties as the amount of adaptation data increases. On the other hand, direct adaptation approaches, like maximum a posteriori (MAP) estimation have nice asymptotic properties but provide only a moderate improvement when the amount of adaptation data is small. In this work, we jointly optimize a direct and indirect adaptation to take advantage of both approaches. Contrary to published approaches where direct and indirect adaptation are performed one after the other with a very loose interaction and no joint estimation criterion, we propose to estimate a MLLR-like transformation as well as the HMM mean vectors simultaneously, using a MAP estimation criterion. The optimal interaction between the direct and indirect adaptation associated with the prior knowledge provided by the MAP criterion leads to improvement over MLLR and MAP for all size of adaptation data evaluated.