On using a priori segmentation of the speech signal in an N-best solutions post-processing

This paper proposes a new approach to the incorporation of automatic a-priori segmentation into an HMM based speech recognizer. The approach used for the post-processing of N-best solutions is based on stochastic modelling of the number of speech signal stationarity changes which occur within the phonetic segments of each solution. The objective of this post-processing is to validate the presence of stationarity zones in the speech signal. This particular validation cannot be exploited using a centisecond approach. The signal stationarity changes are detected using an "a priori" segmentation algorithm. Two phonetic models are calculated for each phonetic segment. One corresponds to correct solutions and the other one corresponds to incorrect solutions. These two models are used simultaneously in order to compute a post-processing score for each solution. In the initial set of experiments, which was conducted on telephone databases, the use of this method resulted in a 9% error rate reduction on the "Number" database, and a 15% error rate reduction on the "Digit" database.