Combining Bayesian learning and vector field smoothing for on-line incremental speaker adaptation

In this paper we investigate the combination of Bayesian Learning (also known as Maximum A Posteriori-MAP) and Vector Field Smoothing (VFS) to the on-line incremental speaker adaptation of Continuous Density Hidden Markov Models (CDHMMs). The parameters of the Gaussian mixture output densities are adapted during the MAP step, using the exponential… (More)