Fast nonlinear time alignment for isolated word recognition

Abstract

A fast nonlinear time alignment method is presented, which is based on a preprocessing of the normalized speech spectrogram by means of a segmentation of the trace in the spectral feature space. After such trace segmentation the patterns have a fixed format and allow for a subsequent classification with a distance measure which is obtained from conventional dynamic programming with extreme constraints. Since, due to the trace segmentation preprocessing, these extreme constraints can be applied without performance degradation, the described method offers savings in computing time by a factor of 10 or more as compared to conventional dynamic programming. As a side benefit, reference pattern memory savings by a factor of 3 or more are obtained.