Towards the supervised machine learning and the conceptual segmentation technique in the spontaneous Arabic speech understanding
Younés Bahou, M. Maâloul, Emna Boughariou · Procedia Computer Science · 2017
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].