Automatic segmentation of continuous speech signals

A statistical approach of the automatic segmentation of the speech signal is discussed. The purpose is to detect acoustic events which reveal articulatory changes as voice or frication onset and termination, closure, release... and formantic variations. The main idea is to model the signal by a statistical model (AR, ARMA) and to use test statistics (generalized likelihood, statistics of cumulative sum type) to detect sequentially abrupt changes in the parameters of the model. In the three segmentations which are presented here, the identification and testing procedures are sequential and monitored after every sample to obtain a better precision of change time estimations. The results obtained by each one are similar and speaker-independent. The detected acoustic events define interesting infra-phonemic units.