# A Simple HMM with Self-Supervised Representations for Phone Segmentation

arXiv: https://arxiv.org/abs/2409.09646 · HTML: https://mlsanity.com/paper/2409.09646/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 15 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [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)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation](https://mlsanity.com/paper/2007.13465/) (2020)
- [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)
- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words](https://mlsanity.com/paper/2110.15909/) (2021)
- [Aligned Contrastive Predictive Coding](https://mlsanity.com/paper/2104.11946/) (2021)
- [Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding](https://mlsanity.com/paper/2110.02345/) (2021)
- [Phoneme Segmentation Using Self-Supervised Speech Models](https://mlsanity.com/paper/2211.01461/) (2022)
- [A predictive learning model can simulate temporal dynamics and context effects found in neural representations of continuous speech](https://mlsanity.com/paper/2405.08237/) (2024)
- [Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models](https://mlsanity.com/paper/2210.16659/) (2022)
- [Towards Matching Phones and Speech Representations](https://mlsanity.com/paper/2310.17558/) (2023)

## Built on by (most cited first, 1 of 1)

- [Phone Segmentation and Recognition through Phonological Activation Mapping](https://mlsanity.com/paper/2607.09020/) (2026)

## Abstract (arXiv)

Despite the recent advance in self-supervised representations, unsupervised phonetic segmentation remains challenging. Most approaches focus on improving phonetic representations with self-supervised learning, with the hope that the improvement can transfer to phonetic segmentation. In this paper, contrary to recent approaches, we show that peak detection on Mel spectrograms is a strong baseline, better than many self-supervised approaches. Based on this finding, we propose a simple hidden Markov model that uses self-supervised representations and features at the boundaries for phone segmentation. Our results demonstrate consistent improvements over previous approaches, with a generalized formulation allowing versatile design adaptations.