# Phoneme Boundary Detection using Learnable Segmental Features

arXiv: https://arxiv.org/abs/2002.04992 · HTML: https://mlsanity.com/paper/2002.04992/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [Phoneme Segmentation Using Self-Supervised Speech Models](https://mlsanity.com/paper/2211.01461/) (2022)
- [Unsupervised Automatic Speech Recognition: A Review](https://mlsanity.com/paper/2106.04897/) (2021)
- [Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models](https://mlsanity.com/paper/2210.16659/) (2022)
- [Unsupervised Speech Segmentation: A General Approach Using Speech Language Models](https://mlsanity.com/paper/2501.03711/) (2025)
- [Fully Differentiable Neural Forced Alignment via Soft Dynamic Programming](https://mlsanity.com/paper/2606.25460/) (2026)

## Abstract (arXiv)

Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc. In this work, we propose a neural architecture coupled with a parameterized structured loss function to learn segmental representations for the task of phoneme boundary detection. First, we evaluated our model when the spoken phonemes were not given as input. Results on the TIMIT and Buckeye corpora suggest that the proposed model is superior to the baseline models and reaches state-of-the-art performance in terms of F1 and R-value. We further explore the use of phonetic transcription as additional supervision and show this yields minor improvements in performance but substantially better convergence rates. We additionally evaluate the model on a Hebrew corpus and demonstrate such phonetic supervision can be beneficial in a multi-lingual setting.