A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech
This work addresses the challenge of enhancing semantic analysis for spontaneous Arabic speech, which is incremental as it builds on existing models within the 'Oréodule' project.
The researchers tackled the problem of improving the probabilistic model for a semantic decoder in automatic speech recognition for Arabic by testing the influence of pertinent context and contextual data integration, resulting in satisfactory findings.
This work is part of a large research project entitled "Oréodule" aimed at developing tools for automatic speech recognition, translation, and synthesis for Arabic language. Our attention has mainly been focused on an attempt to improve the probabilistic model on which our semantic decoder is based. To achieve this goal, we have decided to test the influence of the pertinent context use, and of the contextual data integration of different types, on the effectiveness of the semantic decoder. The findings are quite satisfactory.