Temporal decomposition: a framework for enhanced speech recognition

Short term spectral analysis of source-filter modeling gives a parameterized description of the acoustic signal in terms of a sequence of vectors. These parameter vectors change slowly with time corresponding to a slowly moving vocal tract. The authors consider a model (temporal decomposition) that approximates the time variation by a set of target vectors and interpolation functions that overlap in time. They present a geometric interpretation of the approach, describe an algorithm for decomposing a given utterance into parameters of such a model, and discuss how such modeling can be used in speech recognition systems.<<ETX>>