Abstract: Speech processing is obtained that, given a probabilistic mapping between static speech sounds and pseudo-articulator positions, allows sequences of speech sounds to be mapped to smooth sequences of pseudo-articulator positions. In addition, a method for learning a probabilistic mapping between static speech sounds and pseudo-articulator position is described. The method for learning the mapping between static speech sounds and pseudo-articulator position uses a set of training data composed only of speech sounds. The said speech processing can be applied to various speech analysis tasks, including speech recognition, speaker recognition, speech coding, speech synthesis, and voice mimicry.

Abstract: A method of modeling symbol sequence generation is obtained which allows the probability of symbol sequences to be estimated. The model involves treating symbols as if they are emitted as a point travels through an abstract space called a continuity map (CM), in which each position in the space has associated probabilities of emitting each of the possible symbols. This method for modeling symbol generation, combined with methods for estimating the probability of symbol sequences given smooth paths through the CM, can be applied to such problems as language modeling and anomaly/fraud detection. A fraud detection study is described that demonstrates that the invention can be used for detecting medical fraud.