Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909768293023744 |
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| author | Zezario, Ryandhimas E. |
| author_facet | Zezario, Ryandhimas E. |
| contents | This paper provides an overview of recent progress in non-intrusive speech intelligibility prediction for hearing aids (HA). We summarize developments in robust acoustic feature extraction, hearing loss modeling, and the use of emerging architectures for long-sequence processing. Listener-specific adaptation strategies and domain generalization approaches that aim to improve robustness in unseen acoustic environments are also discussed. Remaining challenges, such as the need for large-scale, diverse datasets and reliable cross-profile generalization, are acknowledged. Our goal is to offer a perspective on current trends, ongoing challenges, and possible future directions toward practical and reliable HA-oriented intelligibility prediction systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges Zezario, Ryandhimas E. Audio and Speech Processing Sound This paper provides an overview of recent progress in non-intrusive speech intelligibility prediction for hearing aids (HA). We summarize developments in robust acoustic feature extraction, hearing loss modeling, and the use of emerging architectures for long-sequence processing. Listener-specific adaptation strategies and domain generalization approaches that aim to improve robustness in unseen acoustic environments are also discussed. Remaining challenges, such as the need for large-scale, diverse datasets and reliable cross-profile generalization, are acknowledged. Our goal is to offer a perspective on current trends, ongoing challenges, and possible future directions toward practical and reliable HA-oriented intelligibility prediction systems. |
| title | Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2509.03017 |