# Perceptimatic: A human speech perception benchmark for unsupervised subword modelling

arXiv: https://arxiv.org/abs/2010.05961 · HTML: https://mlsanity.com/paper/2010.05961/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

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

- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [Do self-supervised speech models develop human-like perception biases?](https://mlsanity.com/paper/2205.15819/) (2022)
- [Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representations](https://mlsanity.com/paper/2604.02102/) (2026)
- [Predicting non-native speech perception using the Perceptual Assimilation Model and state-of-the-art acoustic models](https://mlsanity.com/paper/2205.15823/) (2022)
- [What Do Humans Hear When Interacting? Experiments on Selective Listening for Evaluating ASR of Spoken Dialogue Systems](https://mlsanity.com/paper/2508.04402/) (2025)
- [Do Self-Supervised Speech Models Exhibit the Critical Period Effects in Language Acquisition?](https://mlsanity.com/paper/2508.21210/) (2025)

## Abstract (arXiv)

In this paper, we present a data set and methods to compare speech processing models and human behaviour on a phone discrimination task. We provide Perceptimatic, an open data set which consists of French and English speech stimuli, as well as the results of 91 English- and 93 French-speaking listeners. The stimuli test a wide range of French and English contrasts, and are extracted directly from corpora of natural running read speech, used for the 2017 Zero Resource Speech Challenge. We provide a method to compare humans' perceptual space with models' representational space, and we apply it to models previously submitted to the Challenge. We show that, unlike unsupervised models and supervised multilingual models, a standard supervised monolingual HMM-GMM phone recognition system, while good at discriminating phones, yields a representational space very different from that of human native listeners.