Whispering Wisely: Smart Subset Selection for ASR Fine-Tuning

The paper proposes an embedding‑based subset selection method for fine‑tuning Automatic Speech Recognition models, specifically using Whisper’s pretrained encoder to map audio samples into a fixed‑dimensional space and applying a greedy k‑center algorithm to pick diverse, representative subsets. Experiments on Hebrew and Danish show that k‑center selection outperforms random sampling, especially when the training set is small, and that the chosen subsets exhibit greater lexical diversity. The results suggest that this practical strategy can improve ASR performance under limited data budgets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• rag
• fine-tuning
• multimodal