HMM-based systems for Automatic Speech Recognition typically model the acoustic features using mixtures of multivariate Gaussians. In this thesis, we consider the problem of learning a suitable covariance matrix for each Gaussian. A variety of schemes have been proposed for controlling the number of covariance parameters per Gaussian, and studies have shown that in general, the greater the number of parameters used in the models, the better the recognition performance. We therefore investigate… CONTINUE READING

IEEE Transactions on Pattern Analysis and Machine Intelligence • 1983

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A decision theoretic formulation of a training problem in speech recognition and a comparison of training by unconditional versus conditional maximum likelihood

A. Nádas

IEEE transactions on Acoustics, Speech and Signal Processing , 31, 814–817. 7.1.2, 7.3.2 • 1983