# Objective Intelligibility Prediction Using Distance Metrics on Speech Foundation Model Representations

arXiv: https://arxiv.org/abs/2609.13046 · HTML: https://mlsanity.com/paper/2609.13046/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

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

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Advocating Character Error Rate for Multilingual ASR Evaluation](https://mlsanity.com/paper/2410.07400/) (2024)
- [KAD: No More FAD! An Effective and Efficient Evaluation Metric for Audio Generation](https://mlsanity.com/paper/2502.15602/) (2025)
- [The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains](https://mlsanity.com/paper/2310.02640/) (2023)
- [A Study on Incorporating Whisper for Robust Speech Assessment](https://mlsanity.com/paper/2309.12766/) (2023)
- [Speech foundation models on intelligibility prediction for hearing-impaired listeners](https://mlsanity.com/paper/2401.14289/) (2024)
- [Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction](https://mlsanity.com/paper/2312.15616/) (2023)
- [Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People](https://mlsanity.com/paper/2505.08215/) (2025)
- [Layer-wise Analysis for Quality of Multilingual Synthesized Speech](https://mlsanity.com/paper/2509.04830/) (2025)
- [Towards Evaluating Generative Audio: Insights from Neural Audio Codec Embedding Distances](https://mlsanity.com/paper/2509.18823/) (2025)
- [From Hallucination to Articulation: Language Model-Driven Losses for Ultra Low-Bitrate Neural Speech Coding](https://mlsanity.com/paper/2602.06213/) (2026)
- [Assessing the Impact of Noise and Speech Enhancement on the Intelligibility of Speech Codecs](https://mlsanity.com/paper/2605.03776/) (2026)

## Abstract (arXiv)

High-dimensional representations of pretrained speech foundation models have proven beneficial for objective speech quality and intelligibility prediction. While existing work on neural intelligibility prediction usually leverages such representations for task-specific fine-tuning, in this work we evaluate the usefulness of such representations for intelligibility prediction without any further training. We conduct a layer-wise analysis of multiple speech foundation models, correlating various embedding distances with subjective intelligibility scores. The results show that embeddings extracted from Whisper speech recognition models are best suited, with the last encoder and decoder layers yielding the best correlations when using the Fr'echet Audio Distance. Notably, the evaluated distances outperform classical intelligibility metrics and are more robust than Word and Character Error Rates. Further, correlations improve with increasing size of the Whisper model from which embeddings are extracted.