Wided Bouchelligua

ABSTRACT 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.

1. Introduction In recent years, speech emotion recognition (SER) has garnered significant attention due to its applications in human–computer interaction [1], psychological assessment [2], and entertainment technologies such as robotics [3] and computer games [4]. The ability of machines to detect and interpret human emotions from speech enhances user experience, enabling more empathetic and natural interactions in various domains, such as virtual assistants and automated customer service.