# Blind phoneme segmentation with temporal prediction errors

arXiv: https://arxiv.org/abs/1608.00508 · HTML: https://mlsanity.com/paper/1608.00508/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2016.

## Built on by (most cited first, 9 of 9)

- [Segmental Contrastive Predictive Coding for Unsupervised Word Segmentation](https://mlsanity.com/paper/2106.02170/) (2021)
- [Variable-rate discrete representation learning](https://mlsanity.com/paper/2103.06089/) (2021)
- [Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring](https://mlsanity.com/paper/2202.11929/) (2022)
- [A Brief Overview of Unsupervised Neural Speech Representation Learning](https://mlsanity.com/paper/2203.01829/) (2022)
- [Unsupervised Automatic Speech Recognition: A Review](https://mlsanity.com/paper/2106.04897/) (2021)
- [Unsupervised Symbolic Music Segmentation using Ensemble Temporal Prediction Errors](https://mlsanity.com/paper/2207.00760/) (2022)
- [Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network](https://mlsanity.com/paper/2207.14568/) (2022)
- [Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priors](https://mlsanity.com/paper/2603.08359/) (2026)
- [Representing Speech Through Autoregressive Prediction of Cochlear Tokens](https://mlsanity.com/paper/2508.11598/) (2025)

## Abstract (arXiv)

Phonemic segmentation of speech is a critical step of speech recognition systems. We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chains and recurrent neural network. Our approach consists in analyzing the error profile of a model trained to predict speech features frame-by-frame. Specifically, we try to learn the dynamics of speech in the MFCC space and hypothesize boundaries from local maxima in the prediction error. We evaluate our system on the TIMIT dataset, with improvements over similar methods.