# Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners

arXiv: https://arxiv.org/abs/2509.16979 · HTML: https://mlsanity.com/paper/2509.16979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [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)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://mlsanity.com/paper/2305.05084/) (2023)
- [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://mlsanity.com/paper/2304.06795/) (2023)
- [MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra](https://mlsanity.com/paper/2305.13686/) (2023)
- [FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancement](https://mlsanity.com/paper/2206.07293/) (2022)
- [Granary: Speech Recognition and Translation Dataset in 25 European Languages](https://mlsanity.com/paper/2505.13404/) (2025)
- [Speech foundation models on intelligibility prediction for hearing-impaired listeners](https://mlsanity.com/paper/2401.14289/) (2024)
- [ZipEnhancer: Dual-Path Down-Up Sampling-based Zipformer for Monaural Speech Enhancement](https://mlsanity.com/paper/2501.05183/) (2025)
- [Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People](https://mlsanity.com/paper/2505.08215/) (2025)
- [Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges](https://mlsanity.com/paper/2509.03017/) (2025)

## Abstract (arXiv)

Speech intelligibility evaluation for hearing-impaired (HI) listeners is essential for assessing hearing aid performance, traditionally relying on listening tests or intrusive methods like HASPI. However, these methods require clean reference signals, which are often unavailable in real-world conditions, creating a gap between lab-based and real-world assessments. To address this, we propose a non-intrusive intelligibility prediction framework that leverages speech enhancers to provide a parallel enhanced-signal pathway, enabling robust predictions without reference signals. We evaluate three state-of-the-art enhancers and demonstrate that prediction performance depends on the choice of enhancer, with ensembles of strong enhancers yielding the best results. To improve cross-dataset generalization, we introduce a 2-clips augmentation strategy that enhances listener-specific variability, boosting robustness on unseen datasets. Our approach consistently outperforms the non-intrusive baseline, CPC2 Champion across multiple datasets, highlighting the potential of enhancer-guided non-intrusive intelligibility prediction for real-world applications.