End-to-End Transformer-based Automatic Speech Recognition for Northern Kurdish: A Pioneering Approach

• URL: http://arxiv.org/abs/2410.16330v1
• Date: Sat, 19 Oct 2024 11:46:30 GMT
• Title: End-to-End Transformer-based Automatic Speech Recognition for Northern Kurdish: A Pioneering Approach
• Authors: Abdulhady Abas Abdullah, Shima Tabibian, Hadi Veisi, Aso Mahmudi, Tarik Rashid,
• Abstract summary: This paper introduces a study exploring the effectiveness of Whisper, a pre-trained ASR model, for Northern Kurdish (Kurmanji) an under-resourced language spoken in the Middle East. Using a Northern Kurdish fine-tuning speech corpus containing approximately 68 hours of validated transcribed data, our experiments demonstrate that the additional module fine-tuning strategy significantly improves ASR accuracy.
• Score: 1.3689715712707342
• License:
• Abstract: Automatic Speech Recognition (ASR) for low-resource languages remains a challenging task due to limited training data. This paper introduces a comprehensive study exploring the effectiveness of Whisper, a pre-trained ASR model, for Northern Kurdish (Kurmanji) an under-resourced language spoken in the Middle East. We investigate three fine-tuning strategies: vanilla, specific parameters, and additional modules. Using a Northern Kurdish fine-tuning speech corpus containing approximately 68 hours of validated transcribed data, our experiments demonstrate that the additional module fine-tuning strategy significantly improves ASR accuracy on a specialized test set, achieving a Word Error Rate (WER) of 10.5% and Character Error Rate (CER) of 5.7% with Whisper version 3. These results underscore the potential of sophisticated transformer models for low-resource ASR and emphasize the importance of tailored fine-tuning techniques for optimal performance.

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