# HASA-net: A non-intrusive hearing-aid speech assessment network

arXiv: https://arxiv.org/abs/2111.05691 · HTML: https://mlsanity.com/paper/2111.05691/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 10 Nov 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [MOSNet: Deep Learning based Objective Assessment for Voice Conversion](https://mlsanity.com/paper/1904.08352/) (2019)
- [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://mlsanity.com/paper/1808.05344/) (2018)
- [MBNet: MOS Prediction for Synthesized Speech with Mean-Bias Network](https://mlsanity.com/paper/2103.00110/) (2021)
- [STOI-Net: A Deep Learning based Non-Intrusive Speech Intelligibility Assessment Model](https://mlsanity.com/paper/2011.04292/) (2020)

## Built on by (most cited first, 4 of 4)

- [MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids](https://mlsanity.com/paper/2204.03305/) (2022)
- [MTI-Net: A Multi-Target Speech Intelligibility Prediction Model](https://mlsanity.com/paper/2204.03310/) (2022)
- [HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids](https://mlsanity.com/paper/2401.01145/) (2024)
- [Predicting and Explaining Hearing Aid Usage Using Encoder-Decoder with Attention Mechanism and SHAP](https://mlsanity.com/paper/2405.11275/) (2024)

## Abstract (arXiv)

Without the need of a clean reference, non-intrusive speech assessment methods have caught great attention for objective evaluations. Recently, deep neural network (DNN) models have been applied to build non-intrusive speech assessment approaches and confirmed to provide promising performance. However, most DNN-based approaches are designed for normal-hearing listeners without considering hearing-loss factors. In this study, we propose a DNN-based hearing aid speech assessment network (HASA-Net), formed by a bidirectional long short-term memory (BLSTM) model, to predict speech quality and intelligibility scores simultaneously according to input speech signals and specified hearing-loss patterns. To the best of our knowledge, HASA-Net is the first work to incorporate quality and intelligibility assessments utilizing a unified DNN-based non-intrusive model for hearing aids. Experimental results show that the predicted speech quality and intelligibility scores of HASA-Net are highly correlated to two well-known intrusive hearing-aid evaluation metrics, hearing aid speech quality index (HASQI) and hearing aid speech perception index (HASPI), respectively.