A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech

10/08/2016
by   Mourad Mars, et al.
0

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.

READ FULL TEXT

page 6

page 10

research
06/07/2023

Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based Augmentation

Despite major advancements in Automatic Speech Recognition (ASR), the st...
research
05/22/2017

Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition

With the evolution of neural network based methods, automatic speech rec...
research
11/29/2019

Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset

We present an experimental dataset, Basic Dataset for Sorani Kurdish Aut...
research
12/25/2021

Multi-Dialect Arabic Speech Recognition

This paper presents the design and development of multi-dialect automati...
research
01/21/2021

Arabic Speech Recognition by End-to-End, Modular Systems and Human

Recent advances in automatic speech recognition (ASR) have achieved accu...
research
07/11/2013

Genetic approach for arabic part of speech tagging

With the growing number of textual resources available, the ability to u...
research
04/07/2022

Arabic Text-To-Speech (TTS) Data Preparation

People may be puzzled by the fact that voice over recordings data sets e...

Please sign up or login with your details

Forgot password? Click here to reset