Abstract
The output probability distributions (PDs) in each state of a discrete HMM suffer from sparseness, causing inaccurate modeling of probabilistic characteristics of speech features within the state. A desirable solution to the problem arising from insufficient training data is to interpolate a maximum likelihood (ML) estimate of a PD with some other estimates that are, to some extent, able to strengthen the robustness of the PD with respect to unseen data. We propose a statistically reliable deleted interpolation (DI) approach. The DI is an efficient technique for interpolating several probability distribution (PD) estimates, and usually different interpolating weights are used for each predetermined range of PD counts. Our approach attempts to piecewise linearly approximate the interpolating weight curve based on some reasoning concerned with statistical reliability of sample-based estimates.
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