Automatic segmentation of Arabic speech signals by HMM and ANN
Ahcène Abed, Aissa Amrouche, Delmadji Abdelkader, Khadidja Nesrine Boubakeur, Ghania Droua-Hamdani · 2016
In this paper, we propose an automatic segmentation system of speech into phonemes for the Arabic language. This segmentation is based on two different techniques : Hidden Markov Models (HMM) and Artificial Neural Networks (ANN). Both systems were used to classify the speech signals, extracted from ALGASD corpus (ALGerian Arabic Speech Database), into five classes : fricatives, plosives, nasals, liquids and vowels. These methods achieve important performances with advantage of ANN.