Overview

Perfect — here’s a rewritten abstract and overview with the modular breakdown and explicit mention of bidirectional translations across English, French, N’ko, and Bambara.

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Abstract

This project develops a modular multilingual system for translation and speech processing in low-resource West African languages, focusing on N’ko and Bambara, while bridging them with French and English. Using RobotsMali/bam-asr-early as the foundational ASR dataset, the system integrates speech recognition (ASR), translation, and speech synthesis (TTS) within a unified pipeline. Built on self-supervised speech models (wav2vec 2.0) and multilingual transformers (mBART, mT5, or LLaMA derivatives), it supports bidirectional translation across all language pairs: English ↔ French, English ↔ N’ko, English ↔ Bambara, French ↔ N’ko, and French ↔ Bambara. The framework is designed to handle script-aware tokenization for N’ko, grammar-sensitive fine-tuning, and mixture-of-experts routing for language-specific adaptation. Beyond immediate performance, the system is structured for iterative learning, improving over time as new community-driven data is added. The outcome is a scalable, accessible platform that strengthens literacy, education, and cultural preservation in West Africa while advancing research in multilingual low-resource NLP.

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Project Overview

1. Problem Statement

West African languages such as N’ko and Bambara face limited digital resources and weak representation in NLP systems. Key challenges include:
• Scarcity of annotated data, especially parallel corpora.
• Lack of robust ASR datasets outside RobotsMali.
• Unique script handling for N’ko (Unicode segmentation, grammar).
• Cross-lingual gaps, especially for English ↔ Bambara and French ↔ N’ko translation.

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2. Objectives
• Build a modular pipeline combining ASR, translation, and TTS.
• Support bidirectional translation across English, French, N’ko, and Bambara.
• Enable both text-based and speech-based interaction.
• Ensure N’ko script fidelity through custom tokenization.
• Scale iteratively, improving as more data is added.

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3. Core Dataset: RobotsMali/bam-asr-early
• Total Duration: 37.41 hours
• Samples: 38,769 (Train: 37,306 | Test: 1,463)
• Subsets:
• Oza’s Bambara-ASR: ~29 hours
• Jeli-ASR-RMAI: ~3.5 hours
• oza-tts-mali-pense: ~4 hours
• Reading-tutor-data: ~1 hour

Columns:
• audio: waveform + metadata
• audioduration: float (0.42–54.6s)
• bam: Bambara transcription
• french: aligned French translation

This dataset anchors the ASR/translation backbone for Bambara ↔ French, with expansion to N’ko and English through transfer learning and additional corpora.

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4. Modular System Architecture

(a) Speech Processing Layer
• ASR: wav2vec 2.0 extracts features → text transcription.
• TTS: speech synthesis for N’ko, Bambara, and French.

(b) Text Processing Layer
• Tokenizer: N’ko Unicode-aware segmentation.
• Grammar adaptation: rules + monolingual fine-tuning.

(c) Core Translation Layer
• Multilingual transformer backbone LLaMA.
• Mixture of experts: specialized pathways for each language pair.

(d) Cross-Lingual Bridge
• Supports all bidirectional translations:
• English ↔ French
• English ↔ N’ko
• English ↔ Bambara
• French ↔ N’ko
• French ↔ Bambara

(e) Learning & Feedback Loop
• Continuous fine-tuning with new data (community, domain-specific).
• Evaluation metrics for ASR/translation/TTS updated iteratively.

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5. Expected Outcomes
• High-quality ASR for Bambara with French alignment.
• Robust N’ko script translation with grammar consistency.
• Speech-based accessibility for education and communication.
• Open-source release of models + community-friendly APIs.

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6. Impact
• Education: Multilingual access to knowledge in native scripts.
• Culture: Stronger digital presence for N’ko and Bambara.
• Research: Provides benchmarks and resources for low-resource NLP.
• Community: Scalable, open, and adaptive system for real-world use.

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Mohamed, would you like me to redraw the architecture diagram to reflect these smaller modular components (with the layers a–e) instead of the single flow I showed earlier? That way, it matches this overview exactly.