Speech recognition with a neural network trace-segmentation

Trace-segmentation (TS) is a method for nonlinear time-normalization of a sequence of speech representation frames prior to recognition of the sequence. It has been shown in a recent work [1] that an Individual Trace-Segmentation (ITS), i.e. a separate segmentation of the trajectory described by each individual coefficient in the speech frame leads to a much improved recognition which exceeds the performance provided by DTW recognition on the same database. This paper describes a follow on work on the ITS technique where a Multi-layer Perceptron has been used to perform an internal mapping in the original ITS input space in order to provide a tighter set of clusters of the speech sequences. This novel technique is called Neural Network Trace-Segmentation (NNTS) and has produced a significant improvement on the ITS original performance.