Hidden Markov model decomposition of speech and noise

The problem of automatic speech recognition in the presence of interfering signals and noise with statistical characteristics ranging from stationary to fast changing and impulsive is discussed. A technique of signal decomposition using hidden Markov models is described. This is a generalization of conventional hidden Markov modeling that provides an optimal method of decomposing simultaneous processes. The technique exploits the ability of hidden Markov models to model dynamically varying signals in order to accommodate concurrent processes, including interfering signals as complex as speech. This form of signal decomposition has wide implications for signal separation in general and improved speech modeling in particular. The application of decomposition to the problem of recognition of speech contaminated with noise is emphasized