Dividing the distributions of HMM and linear interpolation in speech recognition

The authors present an explicit criterion for deciding whether to divide the hidden Markov model (HMM)-based phone model into more precise (for example, context dependent) models, or not. They also discuss the cases when linear interpolations of these models are used, and present an explicit solution of the interpolation weight lambda . These criteria are obtained by evaluating the estimation errors of the distributions of these models. The authors show that the estimation errors should be evaluated by the projected covariance matrices of the estimation errors of the logarithm of the probabilities.<
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