Towards the supervised machine learning and the conceptual segmentation technique in the spontaneous Arabic speech understanding

The understanding task of an utterance meaning depends mostly on its concepts extraction. In this paper, we propose a method for the spontaneous Arabic speech understanding, in particular a conceptual segmentation of spontaneous Arabic oral utterances. It takes a transcribed utterance as input and provides conceptual labels as output in the form of a set of Conceptual Segments (CSs). This method is a part of the numerical approach and is based on supervised machine learning (ML) technique. The originality of our work lies in the processing of Out-Of-Vocabulary (OOV) words whether before and/or after the utterance segmentation task. Furthermore, this work is a part of the improvement of the understanding module of SARF system [2]. Indeed, we aim to compare our numerical method with the symbolic one proposed by [2] and the hybrid one proposed by [1].