# MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids

arXiv: https://arxiv.org/abs/2204.03305 · HTML: https://mlsanity.com/paper/2204.03305/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features](https://mlsanity.com/paper/2111.02363/) (2021)
- [STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model](https://mlsanity.com/paper/2011.04292/) (2020)
- [Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders](https://mlsanity.com/paper/2001.01538/) (2020)
- [HASA-net: A non-intrusive hearing-aid speech assessment network](https://mlsanity.com/paper/2111.05691/) (2021)

## Built on by (most cited first, 11 of 11)

- [Non-Intrusive Speech Intelligibility Prediction for Hearing-Impaired Users using Intermediate ASR Features and Human Memory Models](https://mlsanity.com/paper/2401.13611/) (2024)
- [Speech foundation models on intelligibility prediction for hearing-impaired listeners](https://mlsanity.com/paper/2401.14289/) (2024)
- [Non-Intrusive Speech Intelligibility Prediction for Hearing Aids using Whisper and Metadata](https://mlsanity.com/paper/2309.09548/) (2023)
- [Non Intrusive Intelligibility Predictor for Hearing Impaired Individuals using Self Supervised Speech Representations](https://mlsanity.com/paper/2307.13423/) (2023)
- [HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids](https://mlsanity.com/paper/2401.01145/) (2024)
- [NeuroAMP: A Novel End-to-end General Purpose Deep Neural Amplifier for Personalized Hearing Aids](https://mlsanity.com/paper/2502.10822/) (2025)
- [Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model](https://mlsanity.com/paper/2308.09262/) (2023)
- [EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events](https://mlsanity.com/paper/2309.01480/) (2023)
- [No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction](https://mlsanity.com/paper/2506.02039/) (2025)
- [Non-Intrusive Binaural Speech Intelligibility Prediction Using Mamba for Hearing-Impaired Listeners](https://mlsanity.com/paper/2507.05729/) (2025)
- [Bottleneck Transformer-Based Approach for Improved Automatic STOI Score Prediction](https://mlsanity.com/paper/2602.15484/) (2026)

## Abstract (arXiv)

Improving the user's hearing ability to understand speech in noisy environments is critical to the development of hearing aid (HA) devices. For this, it is important to derive a metric that can fairly predict speech intelligibility for HA users. A straightforward approach is to conduct a subjective listening test and use the test results as an evaluation metric. However, conducting large-scale listening tests is time-consuming and expensive. Therefore, several evaluation metrics were derived as surrogates for subjective listening test results. In this study, we propose a multi-branched speech intelligibility prediction model (MBI-Net), for predicting the subjective intelligibility scores of HA users. MBI-Net consists of two branches of models, with each branch consisting of a hearing loss model, a cross-domain feature extraction module, and a speech intelligibility prediction model, to process speech signals from one channel. The outputs of the two branches are fused through a linear layer to obtain predicted speech intelligibility scores. Experimental results confirm the effectiveness of MBI-Net, which produces higher prediction scores than the baseline system in Track 1 and Track 2 on the Clarity Prediction Challenge 2022 dataset.