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The maximum likelihood linear regression (MLLR) approach for speaker adaptation of continuous density mixture Gaussian HMMs is presented and its application to static and incremental adaptation for both supervised and unsupervised modes described. The approach involves computing a transformation for the mixture component means using linear regression. To allow adaptation to be performed with limited amounts of data, a small number of transformations are dened and each one is tied to a number of component mixtures. In previous work, the tyings were predetermined based on the amount of available data. Recently we have used dynamic regression class generation which chooses the appropriate number of classes and transform tying during the adaptation phase. This allows complete unsupervised operation with arbitrary adaptation data. Results are given for static supervised adaptation for non-native speakers and also unsupervised incremental adaptation. Both show the e ectiveness and exibility of the MLLR approach.