Adaptive Kernel Canonical Correlation Analysis for Estimation of Task Dynamics from Acoustics
نویسنده
چکیده
We present a method for acoustic-articulatory inversion whose targets are the abstract tract variables from task dynamic theory. Towards this end we construct a non-linear Hammerstein system whose parameters are updated with adaptive kernel canonical correlation analysis. This approach is notably semi-analytical and applicable to large sets of data. Training behaviour is compared across four kernel functions and prediction of tract variables is shown to be significantly more accurate than state-of-the-art mixture density networks.
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تاریخ انتشار 2010