# Unsupervised Word Segmentation from Speech with Attention

arXiv: https://arxiv.org/abs/1806.06734 · HTML: https://mlsanity.com/paper/1806.06734/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Whisper Has an Internal Word Aligner](https://mlsanity.com/paper/2509.09987/) (2025)
- [On the Difficulty of Segmenting Words with Attention](https://mlsanity.com/paper/2109.10107/) (2021)
- [Supervised Attention in Sequence-to-Sequence Models for Speech Recognition](https://mlsanity.com/paper/2204.12308/) (2022)
- [Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings](https://mlsanity.com/paper/2106.04298/) (2021)

## Abstract (arXiv)

We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between recordings in the UL with translations in a well-resourced language. It uses Acoustic Unit Discovery (AUD) to convert speech into a sequence of pseudo-phones that is segmented using neural soft-alignments produced by a neural machine translation model. Evaluation uses an actual Bantu UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the potential of attentional word segmentation for language documentation.