David Nix, John Hogden
We describe Maximum-Likelihood Continuity Mapping (MALCOM), an alternative to hidden Markov models (HMMs) for processing sequence data such as speech. While HMMs have a discrete "hidden" space con(cid:173) strained by a fixed finite-automaton architecture, MALCOM has a con(cid:173) tinuous hidden space-a continuity map-that is constrained only by a smoothness requirement on paths through the space. MALCOM fits into the same probabilistic framework for speech recognition as HMMs, but it represents a more realistic model of the speech production process. To evaluate the extent to which MALCOM captures speech production information, we generated continuous speech continuity maps for three speakers and used the paths through them to predict measured speech articulator data. The median correlation between the MALCOM paths obtained from only the speech acoustics and articulator measurements was 0.77 on an independent test set not used to train MALCOM or the predictor. This unsupervised model achieved correlations over speak(cid:173) ers and articulators only 0.02 to 0.15 lower than those obtained using an analogous supervised method which used articulatory measurements as well as acoustics ..