Los Alamos Natl. Lab., MS B256, Los Alamos, NM 87545
The continuity mapping algorithm---a procedure for learning to recover the relative positions of the articulators from speech signals---is evaluated using human speech data. The advantage of continuity mapping is that it is an unsupervised algorithm; that is, it can potentially be trained to make a mapping from speech acoustics to speech articulation without articulator measurements. The procedure starts by vector quantizing short windows of a speech signal so that each window is represented (encoded) by a single number. Next, multidimensional scaling is used to map quantization codes that were temporally close in the encoded speech to nearby points in a continuity map. Since speech sounds produced sufficiently close together in time must have been produced by similar articulator configurations, and speech sounds produced close together in time are close to each other in the continuity map, sounds produced by similar articulator positions should be mapped to similar positions in the continuity map. The data set used for evaluating the continuity mapping algorithm is comprised of simultaneously collected articulator and acoustic measurements made using an electromagnetic midsagittal articulometer on a human subject. Comparisons between measured articulator positions and those recovered using continuity mapping will be presented.