Mapping Arabic acoustic parameters to their articulatory features using neural networks

Abstract

A mapping system based on an artificial neural network was designed, trained, and tested to map Arabic acoustic parameters to their corresponding articulatory features. The main objective of the study was to find the correlation between these two different types of features. To train and test the system, an in-house database was created for all 29 Arabic alphabets as carrier words for our intended Arabic phonemes. Fifty Arabic native speakers were asked to utter all alphabets 10 times. Hence, the database consisted of 10 repetitions of each alphabet produced by each speaker, resulting in 14,500 tokens. The system was tested to extract Arabic articulatory features using another disjoint speech data subset. The overall accuracy of the system was 64.06% for all articulatory feature elements and all Arabic phonemes.