Hassaniya Arabic Text-To-Speech System Using Transfer Learning
# Hassaniya Arabic Text-To-Speech System Using Transfer Learning
**Development of a Hassaniya Dialect Speech Synthesis System**
*Master M1 — Artificial Intelligence*
*Module: NLP Dialects*
**Mohamed Salem Ebnou Echvagha Oubeid** | ID: C34613
June 2026
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## Overview
This project presents a **proof-of-concept Text-To-Speech (TTS) system** for the **Hassaniya Arabic dialect** — the primary Arabic dialect spoken in Mauritania and parts of Western Sahara, Mali, and Senegal.
Hassaniya is a **low-resource dialect** with very limited digital presence and almost no existing speech synthesis tools. This project explores the feasibility of building a TTS pipeline using **transfer learning** from pretrained Arabic TTS models, rather than training from scratch.
## Objectives
- Build an end-to-end TTS pipeline for Hassaniya Arabic
- Demonstrate transfer learning from pretrained Arabic speech models
- Create a reusable preprocessing and annotation pipeline
- Document challenges of working with low-resource dialects
- Provide a foundation for future Hassaniya speech technology research
## Dataset
| Property | Value |
|----------|-------|
| **Samples** | 294 audio recordings |
| **Format** | Audio bytes + text transcriptions |
| **Language** | Hassaniya Arabic (Mauritanian dialect) |
| **Avg. text length** | ~33 characters |
| **Source** | Collected Hassaniya speech samples |
## Methodology
### Pipeline Architecture
```mermaid
graph LR
A[Raw Text Hassaniya Arabic] --> B[Text Preprocessing Normalization & Cleaning]
B --> C[Tokenization Character/Phoneme Level]
C --> D[TTS Model Pretrained + Fine-tuned]
D --> E[Mel Spectrogram Generation]
E --> F[Vocoder Waveform Synthesis]
F --> G[Generated Speech Audio Output]
```
### Transfer Learning Strategy
```mermaid
graph TD
A[Pretrained Arabic TTS Model] --> B[Feature Extraction Layers Frozen]
A --> C[Output Layers Fine-tunable]
D[Hassaniya Dataset 294 samples] --> E[Fine-tuning Process]
C --> E
E --> F[Hassaniya TTS Mo …