# Spoken-Term Discovery using Discrete Speech Units

arXiv: https://arxiv.org/abs/2408.14390 · HTML: https://mlsanity.com/paper/2408.14390/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2024.

## Built on by (most cited first, 8 of 8)

- [Unsupervised Word Discovery: Boundary Detection with Clustering vs. Dynamic Programming](https://mlsanity.com/paper/2409.14486/) (2024)
- [Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery?](https://mlsanity.com/paper/2507.19204/) (2025)
- [Unsupervised lexicon learning from speech is limited by representations rather than clustering](https://mlsanity.com/paper/2510.09225/) (2025)
- [Towards few-shot isolated word reading assessment](https://mlsanity.com/paper/2507.12217/) (2025)
- [Revisiting Lexicon Evaluation in Unsupervised Word Discovery](https://mlsanity.com/paper/2606.06183/) (2026)
- [Connecting Speech to Words through Images](https://mlsanity.com/paper/2606.16807/) (2026)
- [Recovering the Zipfian Distribution in Unsupervised Term Discovery](https://mlsanity.com/paper/2606.10781/) (2026)
- [Mapping Written Words to Spoken Words in a Different Language Using Only Visual Grounding](https://mlsanity.com/paper/2608.26925/) (2026)

## Abstract (arXiv)

Discovering a lexicon from unlabeled audio is a longstanding challenge for zero-resource speech processing. One approach is to search for frequently occurring patterns in speech. We revisit this idea with DUSTED: Discrete Unit Spoken-TErm Discovery. Leveraging self-supervised models, we encode input audio into sequences of discrete units. Next, we find repeated patterns by searching for similar unit sub-sequences, inspired by alignment algorithms from bioinformatics. Since discretization discards speaker information, DUSTED finds better matches across speakers, improving the coverage and consistency of the discovered patterns. We demonstrate these improvements on the ZeroSpeech Challenge, achieving state-of-the-art results on the spoken-term discovery track. Finally, we analyze the duration distribution of the patterns, showing that our method finds longer word- or phrase-like terms.