On the registration of time and the patterning of speech movements.

In order to study speech coordination we frequently average kinematic and other physiological signals. The averages are assumed to be more representative of the underlying patterns of production than individual records. In this note we outline different approaches to averaging and present a new nonlinear normalization technique that offers better information than ensemble averaging, linear normalization, or feature alignment methods. We suggest that this technique provides a clear estimation of pattern shape while preserving information on the variation over time.