# Embedding-based subset selection improves ASR Fine-Tuning on hebrew and danish

Digest AI · Research · published 2026-10-27T04:00:00Z

Canonical: https://digestai.news/story/embedding-based-subset-selection-improves-asr-fine-tuning-on-hebrew-an

## Summary

Embedding-based subset selection uses Whisper’s pretrained encoder to map audio samples into a fixed‑dimensional embedding space. A greedy k‑center algorithm then selects diverse, representative subsets for fine‑tuning. The authors evaluated the approach on Hebrew and Danish, finding that k‑center subsets outperform random sampling, especially at smaller subset sizes. The method also captures broader lexical diversity, which likely drives the observed recognition gains.

## Key points

- Embedding-based subset selection uses Whisper encoder embeddings and greedy k-center to pick diverse training samples.
- Tested on Hebrew and Danish, k-center subsets outperform random sampling, especially with small subset sizes.
- The method yields broader lexical diversity, leading to better ASR fine-tuning performance.

## Why it matters

The approach reduces the amount of labeled audio needed to fine‑tune costly ASR models, making it easier for researchers and developers to adapt Whisper to new languages or domains with limited data.

## Sources

1. [Whispering Wisely: Smart Subset Selection for ASR Fine-Tuning](https://arxiv.org/abs/2610.32997) (arXiv cs.CL, 2026-10-27, primary source)

## Cite

Digest AI, "Embedding-based subset selection improves ASR Fine-Tuning on hebrew and danish", 27 October 2026, https://digestai.news/story/embedding-based-subset-selection-improves-asr-fine-tuning-on-hebrew-an

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