# DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon

arXiv: https://arxiv.org/abs/2206.11332 · HTML: https://mlsanity.com/paper/2206.11332/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 57 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling](https://mlsanity.com/paper/2011.11588/) (2020)
- [Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech](https://mlsanity.com/paper/1803.08976/) (2018)
- [The Zero Resource Speech Challenge 2017](https://mlsanity.com/paper/1712.04313/) (2017)
- [The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units](https://mlsanity.com/paper/2010.05967/) (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)
- [Truly unsupervised acoustic word embeddings using weak top-down constraints in encoder-decoder models](https://mlsanity.com/paper/1811.00403/) (2018)
- [A segmental framework for fully-unsupervised large-vocabulary speech recognition](https://mlsanity.com/paper/1606.06950/) (2016)
- [Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches](https://mlsanity.com/paper/1611.02550/) (2016)
- [Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks](https://mlsanity.com/paper/2012.07551/) (2020)
- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [Evaluating the reliability of acoustic speech embeddings](https://mlsanity.com/paper/2007.13542/) (2020)
- [Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptation](https://mlsanity.com/paper/2103.10731/) (2021)
- [Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words](https://mlsanity.com/paper/2110.15909/) (2021)
- [Multilingual Jointly Trained Acoustic and Written Word Embeddings](https://mlsanity.com/paper/2006.14007/) (2020)
- [Self-Expressing Autoencoders for Unsupervised Spoken Term Discovery](https://mlsanity.com/paper/2007.13033/) (2020)
- [Acoustic span embeddings for multilingual query-by-example search](https://mlsanity.com/paper/2011.11807/) (2020)
- [Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning](https://mlsanity.com/paper/2204.05148/) (2022)
- [Sampling strategies in Siamese Networks for unsupervised speech representation learning](https://mlsanity.com/paper/1804.11297/) (2018)

## Built on by (most cited first, 12 of 12)

- [What Do Self-Supervised Speech Models Know About Words?](https://mlsanity.com/paper/2307.00162/) (2023)
- [Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model](https://mlsanity.com/paper/2305.11435/) (2023)
- [From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes](https://mlsanity.com/paper/2410.22906/) (2024)
- [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)
- [Revisiting speech segmentation and lexicon learning with better features](https://mlsanity.com/paper/2401.17902/) (2024)
- [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)
- [Tracking the emergence of linguistic structure in self-supervised models learning from speech](https://mlsanity.com/paper/2604.02043/) (2026)
- [Discriminating Form and Meaning in Multilingual Models with Minimal-Pair ABX Tasks](https://mlsanity.com/paper/2505.17747/) (2025)
- [Recovering the Zipfian Distribution in Unsupervised Term Discovery](https://mlsanity.com/paper/2606.10781/) (2026)

## Abstract (arXiv)

Finding word boundaries in continuous speech is challenging as there is little or no equivalent of a 'space' delimiter between words. Popular Bayesian non-parametric models for text segmentation use a Dirichlet process to jointly segment sentences and build a lexicon of word types. We introduce DP-Parse, which uses similar principles but only relies on an instance lexicon of word tokens, avoiding the clustering errors that arise with a lexicon of word types. On the Zero Resource Speech Benchmark 2017, our model sets a new speech segmentation state-of-the-art in 5 languages. The algorithm monotonically improves with better input representations, achieving yet higher scores when fed with weakly supervised inputs. Despite lacking a type lexicon, DP-Parse can be pipelined to a language model and learn semantic and syntactic representations as assessed by a new spoken word embedding benchmark.