Joint Maximum a Posteriori Estimation of Transformation and Hidden Markov Model Parameters

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) are quite efficient when the amount of adaptation data is limited, but have poor asymptotic properties as the amount of 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.