Preliminary study on the shape of speech data manifold in linear signal subspace

Abstract

The data vectors of speech pronunciation signals are projected into the signal subspace. This low-dimensional signal subspace is estimated by the Principal Component Analysis. The geometric shape of the signal manifold in the signal subspace is studied. The correspondence between the change of manifold shape and the change of pronunciation is revealed by experiments, which has potential application in speech signal segmentation.