Smooth interpolation of Gaussian mixture models

The article describes an approach for embodiment of the ambient noise awareness into the statistical model of individual speech units to preserve speech recognizer's robustness under varying environmental conditions. Unlike previous approaches, continuous model for a given range of noise parameters is suggested to allow precise modeling of any predictable ambient noise conditions. The method is based on linked expectation maximization training of a series of Gaussian mixture models in a dense SNR stepping fashion followed by averaging and decimation to reduce storage needs. Final models are interpolated using piecewise cubic Hermite polynomial preserving the shape of the initial model set.