# Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction

arXiv: https://arxiv.org/abs/2204.04288 · HTML: https://mlsanity.com/paper/2204.04288/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2022.

## Built on by (most cited first, 5 of 5)

- [It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition](https://mlsanity.com/paper/2402.05457/) (2024)
- [Non-Intrusive Speech Intelligibility Prediction for Hearing-Impaired Users using Intermediate ASR Features and Human Memory Models](https://mlsanity.com/paper/2401.13611/) (2024)
- [Non Intrusive Intelligibility Predictor for Hearing Impaired Individuals using Self Supervised Speech Representations](https://mlsanity.com/paper/2307.13423/) (2023)
- [Transfer Learning from Whisper for Microscopic Intelligibility Prediction](https://mlsanity.com/paper/2404.01737/) (2024)
- [Non-Intrusive Binaural Speech Intelligibility Prediction Using Mamba for Hearing-Impaired Listeners](https://mlsanity.com/paper/2507.05729/) (2025)

## Abstract (arXiv)

Non-intrusive intelligibility prediction is important for its application in realistic scenarios, where a clean reference signal is difficult to access. The construction of many non-intrusive predictors require either ground truth intelligibility labels or clean reference signals for supervised learning. In this work, we leverage an unsupervised uncertainty estimation method for predicting speech intelligibility, which does not require intelligibility labels or reference signals to train the predictor. Our experiments demonstrate that the uncertainty from state-of-the-art end-to-end automatic speech recognition (ASR) models is highly correlated with speech intelligibility. The proposed method is evaluated on two databases and the results show that the unsupervised uncertainty measures of ASR models are more correlated with speech intelligibility from listening results than the predictions made by widely used intrusive methods.