Probabilistic approach for detection of vocal pathologies in the arabic speech

Abstract

There are different methods for vocal pathology detection. These methods usually have three steps which are feature extraction, feature reduction and speech classification. The first and second steps present obstacles to attain high performance and accuracy of the classification system [20]. Indeed, feature reduction can create a loss of data. In this paper, we present an initial study of Arabic speech classification based on probabilistic approach and distance between reference speeches and speech to classify. The first step in our approach is dedicated to generate a standard distance (phonetic distance) between different healthy speech bases. In the second stage we will determine the distance between speech to classify and reference speeches (phonetic model proper to speaker and a reference phonetic model). Comparing these two distances (distance between speech to classify and reference speeches & standard distance), in the third step, we can classify the input speech to healthy or pathological. The proposed method is able to classify Arabic speeches with an accuracy of 96.25%, and we attain 100% by concatenation falsely classified sequences. Results of our method provide insights that can guide biologists and computer scientists to design high performance systems of vocal pathology detection.