Effective online unsupervised adaptation of Gaussian mixture models and its application to speech classification

Online unsupervised adaptation of statistical classifiers is attractive for many speech processing applications. In this work, we describe an online unsupervised adaptation method for a four-way speech classifier which is based on modelling the universal background model (UBM)–GMM and using confidence scoring in deriving classification results. The aim of the proposed method is to automatically adapt the classifier to mismatched conditions caused by acoustically adverse backgrounds and speaker variability. Extensive analysis of the experimental learning curves shows that the new online unsupervised adaptation algorithm achieves practical convergence. When compared to batch mode adaptation the proposed technique deals effectively with data sparsity and it has significantly lower computational requirements at the expense of a slight sacrifice in classification performance. The proposed algorithm can be readily extended to other mixture families and different expectation–maximization (EM) alternatives for improved performance.