A composite source model for speaker and isolated word recognition

A composite source is an indexed family of random processes (subsources) together with a switch which chooses from among these processes in a stochastic fashion. Such a source has often been proposed as a model for speech and other processes having piece-wise, or quasi, stationary behavior. Until recently, however, very little has been known about such models from either a theoretical or a practical perspective. In this paper, we consider a speaker/isolated word recognition system derived from a composite source model for speech production. In particular, estimates of the underlying subsources are obtained using a modified data compression algorithm. Switch sequences are then derived from these estimates for each utterance. Finally, switch sequences are compared in the time domain (using Levenshtein´s metric) and from a statistical point of view (via variation distance). Both modes of comparison are seen to be highly correlated and produce a recognition procedure with very encouraging results.