A hybrid speech recognizer combining HMMs and polynomial classification

In this paper, we present a hybrid speech recognizer combining Hidden Markov Models (HMMs) and a polynomial classifier. In our approach the emission probabilities are not modeled as a mixture of Gaussians but are calculated by the polynomial classifier. However, we do not apply the classifier directly to the feature vector but we make use of the density values of Gaussians clustering the feature space. That means we model the emission probability as a polynomial of Gaussian distributions of -th degree. As most of these density values are approximately zero for a single feature vector the calculation of a polynomial can be done very efficiently. The usefulness of this hybrid approach was successfully tested on a large conversational speech recognition task.