# Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings

arXiv: https://arxiv.org/abs/2106.04298 · HTML: https://mlsanity.com/paper/2106.04298/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 60 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019)
- [Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders](https://mlsanity.com/paper/1910.12638/) (2019)
- [Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/1612.01744/) (2016)
- [Unsupervised speech representation learning using WaveNet autoencoders](https://mlsanity.com/paper/1901.08810/) (2019)
- [Effectiveness of self-supervised pre-training for speech recognition](https://mlsanity.com/paper/1911.03912/) (2019)
- [Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner](https://mlsanity.com/paper/1607.08723/) (2016)
- [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)
- [The Zero Resource Speech Challenge 2019: TTS without T](https://mlsanity.com/paper/1904.11469/) (2019)
- [Unsupervised Pre-training of Bidirectional Speech Encoders via Masked Reconstruction](https://mlsanity.com/paper/2001.10603/) (2020)
- [An embedded segmental K-means model for unsupervised segmentation and clustering of speech](https://mlsanity.com/paper/1703.08135/) (2017)
- [Speech-to-speech Translation between Untranscribed Unknown Languages](https://mlsanity.com/paper/1910.00795/) (2019)
- [MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible](https://mlsanity.com/paper/1907.12895/) (2019)
- [Towards unsupervised phone and word segmentation using self-supervised vector-quantized neural networks](https://mlsanity.com/paper/2012.07551/) (2020)
- [Unsupervised Discovery of Recurring Speech Patterns Using Probabilistic Adaptive Metrics](https://mlsanity.com/paper/2008.00731/) (2020)
- [A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments](https://mlsanity.com/paper/1710.03501/) (2017)
- [Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery](https://mlsanity.com/paper/1904.03876/) (2019)
- [A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery](https://mlsanity.com/paper/2011.03115/) (2020)
- [Leveraging translations for speech transcription in low-resource settings](https://mlsanity.com/paper/1803.08991/) (2018)
- [Unsupervised Word Segmentation from Speech with Attention](https://mlsanity.com/paper/1806.06734/) (2018)
- [Unwritten Languages Demand Attention Too! Word Discovery with Encoder-Decoder Models](https://mlsanity.com/paper/1709.05631/) (2017)
- [Empirical Evaluation of Sequence-to-Sequence Models for Word Discovery in Low-resource Settings](https://mlsanity.com/paper/1907.00184/) (2019)
- [Investigating Language Impact in Bilingual Approaches for Computational Language Documentation](https://mlsanity.com/paper/2003.13325/) (2020)

## Built on by (most cited first, 2 of 2)

- [Pretraining End-to-End Keyword Search with Automatically Discovered Acoustic Units](https://mlsanity.com/paper/2407.04652/) (2024)
- [LINC: Supporting Language Independent Communication and Comprehension to Enhance Contribution in Multilingual Collaborative Meetings](https://mlsanity.com/paper/2504.18988/) (2025)

## Abstract (arXiv)

Documenting languages helps to prevent the extinction of endangered dialects, many of which are otherwise expected to disappear by the end of the century. When documenting oral languages, unsupervised word segmentation (UWS) from speech is a useful, yet challenging, task. It consists in producing time-stamps for slicing utterances into smaller segments corresponding to words, being performed from phonetic transcriptions, or in the absence of these, from the output of unsupervised speech discretization models. These discretization models are trained using raw speech only, producing discrete speech units that can be applied for downstream (text-based) tasks. In this paper we compare five of these models: three Bayesian and two neural approaches, with regards to the exploitability of the produced units for UWS. For the UWS task, we experiment with two models, using as our target language the Mboshi (Bantu C25), an unwritten language from Congo-Brazzaville. Additionally, we report results for Finnish, Hungarian, Romanian and Russian in equally low-resource settings, using only 4 hours of speech. Our results suggest that neural models for speech discretization are difficult to exploit in our setting, and that it might be necessary to adapt them to limit sequence length. We obtain our best UWS results by using Bayesian models that produce high quality, yet compressed, discrete representations of the input speech signal.