Broad Phonemic Class Segmentation of Speech Signals in Noise Environments

In this paper, we evaluate the performance of an implicit approach for the automatic detection of broad phonemic class boundaries from continuous speech signals in different additive noise environments. We exploit the prior knowledge of glottal pulse locations for the estimation of adjacent broad phonemic class boundaries. The approach’s validity was tested on the DARPA-TIMIT American-English language corpus and NOISEX-92 database. Our framework’s results were very promising since by this method we achieved 25 msec accuracy of 74,9 % for un-noisy environment, while the performance reduced about 5 % for wideband distortion noise. 1