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- W2071543866 abstract "Stochastic word models based on hidden Markov modeling (HMM) techniques are commonly used for speech recognition and are increasingly being used for speech synthesis. An alternate technique for generating stochastic word models using Maximum Likelihood Continuity Mapping (MALCOM) will be presented. MALCOM generates a stochastic model of speech assuming (1) that speech sounds are periodically emitted as a point moves smoothly through a low-dimensional space called a continuity map (CM), and (2) that the sound emitted at time t is a probabilistic function of the position of the point at time t. The assumptions underlying MALCOM are intended to mimic speech production in that (1) speech sounds are produced as the articulators move slowly through a low-dimensional articulator space, and (2) the speech sound produced at time t is a function of the articulator positions at time t. A method for finding CM trajectories (analogous to articulator trajectories) corresponding to a known sequence of phonemes will be shown, and how to use these trajectories as word models for speech recognition and synthesis will be shown. A preliminary test of the theory will be presented in which articulator trajectories estimate given phoneme sequences and phoneme temporal positions are compared to measured articulator trajectories." @default.
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- W2071543866 date "1998-05-01" @default.
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- W2071543866 title "Stochastic word models for articulatorily constrained speech recognition and synthesis" @default.
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- W2071543866 doi "https://doi.org/10.1121/1.421411" @default.
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