We discuss how nonstationarity in observed time series data due to pronounc
ed fluctuations of system parameters can be resolved by making use of embed
ding techniques for scalar data. If a D-dimensional deterministic system is
driven by P slowly lime dependent parameters, a (D + P)-dimensional manifo
ld has to be reconstructed from the scalar time series, which is done by an
tn > 2(D + P)-dimensional time delay embedding. We show that in this space
essential aspects of determinism are restored. We demonstrate the validity
of the idea heuristically, for numerical examples and for human speech dat
a.