A fast algorithm for unsupervised incremental speaker adaptation

Speaker adaptation algorithms often require a rather large amount of adaptation data in order to estimate the new parameters reliably. We investigate how adaptation can be performed in real-time applications with only a few seconds of speech from each user. We propose a modified Bayesian codebook reestimation which does not need the computationally intensive evaluation of normal densities and thus speeds up the adaptation remarkably, e.g. by a factor of 18 for 24-dimensional feature vectors. We performed experiments in two real-time applications with very small amounts of adaptation data, and achieved a word error reduction of up to 11%.