<ul><li>Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA</li><li>The paper discusses the use of a Parameter-Efficient Fine-tuning method called Low-Rank Adaptation (LoRA) to fine-tune a more computationally efficient version of the automatic speech recognition model, Whisper, for aviation communication transcription.</li><li>The authors used the Air Traffic Control Corpus dataset and performed a grid search to optimize the hyperparameters of distil-Whisper using a 5-fold cross-validation.</li><li>The fine-tuned model achieved an average word error rate of 3.86% across five folds, indicating its potential for accurate transcription of aviation communication.</li></ul>

Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA

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