.png){kind=icon}
.png){kind=icon}
Return
Deep learning-based pair barracuda swarm optimization for Arabic text-to-speech synthesizer for visually impaired people using applied linguistics
.png){kind=icon}
PRE
AI
N
M
W
A
DOI：10.1080/02533839.2025.2574445.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a Deep Learning-Based Pair Barracuda Swarm Optimization for an Arabic Text-to-Speech Synthesizer Using Applied Linguistics (DLPBSO-ATTSSAP), designed to support visually impaired individuals. Arabic text-to-speech synthesis is challenging due to linguistic complexity and contextual ambiguity. The proposed system begins with multi-level preprocessing to normalize Arabic text, followed by FastText embeddings to capture semantic and syntactic nuances. A Convolutional Variational Autoencoder (CVAE) is employed to learn latent features for accurate text classification, with hyperparameters optimized through the Pair Barracuda Swarm Optimization (PBSO) algorithm. This enhances classification accuracy and system performance. Finally, the WaveNet model converts processed text into natural, human-like speech. Experimental results show that DLPBSO-ATTSSAP outperforms existing methods across key metrics. By integrating deep learning with swarm optimization, the system provides a user-friendly, efficient, and high-quality speech synthesis solution. This work highlights the model's ability to address language-specific challenges in Arabic and contribute to accessible communication technologies for underrepresented languages.
Keywords:
Arabic language
visually impaired people
text-to-speech synthesizer
pair barracuda swarm optimization
word embedding
computational optimization in engineering
Journal
J
IF:
1.2
Papers:
122
Citations:
1.1K
Organization
Cited Papers