A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech

TL;DR

This paper presents a semantic analyzer designed to improve understanding of spontaneous Arabic speech by enhancing probabilistic models with contextual data integration, leading to better semantic decoding performance.

Contribution

It introduces a novel approach to incorporate various contextual data types into the semantic decoder for spontaneous Arabic speech recognition.

Findings

Improved semantic decoding accuracy with contextual data

Effective integration of different contextual data types

Satisfactory enhancement of probabilistic models

Abstract

This work is part of a large research project entitled "Or\'eodule" 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.