# Unsupervised Discovery of Recurring Speech Patterns Using Probabilistic Adaptive Metrics

arXiv: https://arxiv.org/abs/2008.00731 · HTML: https://mlsanity.com/paper/2008.00731/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Word Discovery in Visually Grounded, Self-Supervised Speech Models](https://mlsanity.com/paper/2203.15081/) (2022)
- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation](https://mlsanity.com/paper/2103.10731/) (2021)
- [Evaluating context-invariance in unsupervised speech representations](https://mlsanity.com/paper/2210.15775/) (2022)
- [Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model](https://mlsanity.com/paper/2305.11435/) (2023)
- [A Masked Segmental Language Model for Unsupervised Natural Language Segmentation](https://mlsanity.com/paper/2104.07829/) (2021)
- [Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery](https://mlsanity.com/paper/2103.08199/) (2021)
- [Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource language](https://mlsanity.com/paper/2106.12834/) (2021)
- [Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning](https://mlsanity.com/paper/2204.05148/) (2022)
- [Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels](https://mlsanity.com/paper/2304.00993/) (2023)
- [Unsupervised Automatic Speech Recognition: A Review](https://mlsanity.com/paper/2106.04897/) (2021)
- [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)
- [Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings](https://mlsanity.com/paper/2106.04298/) (2021)
- [Revisiting Lexicon Evaluation in Unsupervised Word Discovery](https://mlsanity.com/paper/2606.06183/) (2026)

## Abstract (arXiv)

Unsupervised spoken term discovery (UTD) aims at finding recurring segments of speech from a corpus of acoustic speech data. One potential approach to this problem is to use dynamic time warping (DTW) to find well-aligning patterns from the speech data. However, automatic selection of initial candidate segments for the DTW-alignment and detection of "sufficiently good" alignments among those require some type of pre-defined criteria, often operationalized as threshold parameters for pair-wise distance metrics between signal representations. In the existing UTD systems, the optimal hyperparameters may differ across datasets, limiting their applicability to new corpora and truly low-resource scenarios. In this paper, we propose a novel probabilistic approach to DTW-based UTD named as PDTW. In PDTW, distributional characteristics of the processed corpus are utilized for adaptive evaluation of alignment quality, thereby enabling systematic discovery of pattern pairs that have similarity what would be expected by coincidence. We test PDTW on Zero Resource Speech Challenge 2017 datasets as a part of 2020 implementation of the challenge. The results show that the system performs consistently on all five tested languages using fixed hyperparameters, clearly outperforming the earlier DTW-based system in terms of coverage of the detected patterns.