Problem Statement

Standard Arabic speech recognition systems perform poorly on Egyptian Arabic due to significant phonological and lexical differences. Egyptian Arabic lacks standardized orthography, making transcription particularly challenging.

Solution

Fine-tuned Whisper on a curated Egyptian Arabic speech dataset with custom data preprocessing for dialect-specific phonetic patterns. Implemented post-processing rules for handling code-switching between Arabic and English.

Key Features

• ▸Whisper model fine-tuned on Egyptian Arabic
• ▸Custom audio preprocessing pipeline
• ▸Code-switching detection and handling
• ▸Speaker diarization support
• ▸Real-time transcription capability
• ▸Evaluation metrics for dialect-specific accuracy

Challenges

• ⚡Limited labeled data for Egyptian Arabic dialect
• ⚡Handling code-switching between Arabic and English
• ⚡Varied recording quality in training data
• ⚡Non-standardized orthography for dialect transcription

Results & Metrics

Lessons Learned

• 💡Domain-specific fine-tuning of large speech models yields significant accuracy gains
• 💡Data quality matters more than data quantity for low-resource languages
• 💡Post-processing rules can significantly improve transcription quality