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This proposal presents an algorithm called maximum likelihood continuity mapping (MALCOM) which recovers the positions of the tongue, jaw, lips, and other speech articulators from measurements of the sound-pressure waveform of speech. MALCOM differs from other techniques for recovering articulator positions from speech in three critical respects: it does not require training on measured or modeled articulator positions, it does not rely on any particular model of sound propagation through the vocal tract, and it recovers a mapping from acoustics to articulator positions that is linearly, not topographically, related to the actual mapping from acoustics to articulation. The approach categorizes short-time windows of speech into a finite number of sound types, and assumes the probability of using any articulator position to produce a given sound type can be described by a parameterized probability density function. MALCOM then uses maximum likelihood estimation techniques to: (1) find the most likely smooth articulator path given a speech sample and a set of distribution functions (one distribution function for each sound type), and (2) change the parameters of the distribution functions to better account for the data. Using this technique improves the accuracy of articulator position estimates compared to continuity mapping -- the only other technique that learns the relationship between acoustics and articulation solely from acoustics. The technique has potential application to computer speech recognition, speech synthesis and coding, teaching the hearing impaired to speak, improving foreign language instruction, and teaching dyslexics to read. 34 refs., 7 figs

A maximum likelihood approach to estimating articulator positions from speech acoustics