Keyword verification considering the correlation of succeeding feature vectors

The assumption of statistically independent feature vectors within the HMM approach is a well known problem. The aim of this study is to explore a simple and feasible method, that takes the correlation of adjacent feature vectors into account. A so called correlated HMM, that estimates the emission probability of a state with respect to correlated feature vectors, is built by combining two separate knowledge sources. On the one side, a traditional HMM provides an emission probability under the condition of a certain state, whereas on the other side a linear predictor delivers an emission probability considering the previous feature vectors. The efficiency of this method is shown with the help of the German SpeechDat(M) database. The application of the correlated HMM within the verification procedure of a keyword spotter provided an improvement of the figure-of-merit from 87.1% to 88.6%