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Speaker adaptation using constrained estimation of Gaussian mixtures
IEEE Transactions on Speech and Audio Processing
A recent trend in automatic speech recognition systems is the use of continuous mixture-density hidden Markov models (HMM's). Despite the good recognition performance that these systems achieve on average in large vocabulary applications, there is a large variability in performance across speakers. Performance degrades dramatically when the user is radically different from the training population. A popular technique that can improve the performance and robustness of a speech recognition systemdoi:10.1109/89.466659 fatcat:lerucjppwrhphn7axc2wsyg6bi