# Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels

arXiv: https://arxiv.org/abs/2304.00993 · HTML: https://mlsanity.com/paper/2304.00993/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation](https://mlsanity.com/paper/2007.13465/) (2020)
- [Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation](https://mlsanity.com/paper/2106.02170/) (2021)
- [An embedded segmental K-means model for unsupervised segmentation and clustering of speech](https://mlsanity.com/paper/1703.08135/) (2017)
- [A segmental framework for fully-unsupervised large-vocabulary speech recognition](https://mlsanity.com/paper/1606.06950/) (2016)
- [Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks](https://mlsanity.com/paper/2012.07551/) (2020)
- [Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words](https://mlsanity.com/paper/2110.15909/) (2021)
- [Unsupervised Discovery of Recurring Speech Patterns Using Probabilistic Adaptive Metrics](https://mlsanity.com/paper/2008.00731/) (2020)
- [Unsupervised Word Segmentation using K Nearest Neighbors](https://mlsanity.com/paper/2204.13094/) (2022)

## Built on by (most cited first, 7 of 7)

- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [SyllableLM: Learning Coarse Semantic Units for Speech Language Models](https://mlsanity.com/paper/2410.04029/) (2024)
- [Unsupervised Word Discovery: Boundary Detection with Clustering vs. Dynamic Programming](https://mlsanity.com/paper/2409.14486/) (2024)
- [Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models](https://mlsanity.com/paper/2505.17446/) (2025)
- [Towards Unsupervised Speech Recognition at the Syllable-Level](https://mlsanity.com/paper/2510.03639/) (2025)
- [XLS-R fine-tuning on noisy word boundaries for unsupervised speech segmentation into words](https://mlsanity.com/paper/2310.05235/) (2023)
- [Towards Unsupervised Speech Recognition Without Pronunciation Models](https://mlsanity.com/paper/2406.08380/) (2024)

## Abstract (arXiv)

Unsupervised word segmentation in audio utterances is challenging as, in speech, there is typically no gap between words. In a preliminary experiment, we show that recent deep self-supervised features are very effective for word segmentation but require supervision for training the classification head. To extend their effectiveness to unsupervised word segmentation, we propose a pseudo-labeling strategy. Our approach relies on the observation that the temporal gradient magnitude of the embeddings (i.e. the distance between the embeddings of subsequent frames) is typically minimal far from the boundaries and higher nearer the boundaries. We use a thresholding function on the temporal gradient magnitude to define a psuedo-label for wordness. We train a linear classifier, mapping the embedding of a single frame to the pseudo-label. Finally, we use the classifier score to predict whether a frame is a word or a boundary. In an empirical investigation, our method, despite its simplicity and fast run time, is shown to significantly outperform all previous methods on two datasets.