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    <abstract>Cochlear implant (CI) users often experience reduced speech understanding in noisy and reverberant environments. Our recent work has shown the benefits of exploiting knowledge of speech content to drive speech enhancement for CI applications by estimating time-frequency masks based on predictions from a phoneme classifier. However, phonemes with similar temporal and spectral characteristics are likely to be confused, leading to misclassifications that negatively impact the downstream phoneme-based task. Also, machine learning models are generally vulnerable to performance degradation in unseen acoustic conditions. Using a lightweight and causal phoneme-based speech enhancement model developed for CI applications, we demonstrate the utility of alternative phoneme groups to improve model robustness in diverse acoustic settings. Results from objective measures of speech enhancement show that manner of articulation (MOA) groups provide a balance between fine-grained but error-prone phonemes and limited acoustic specificity with voicing groups. In particular, a counterintuitive finding is that MOA-based models trained in reverberant conditions are particularly robust under mismatched train-test conditions. Our findings highlight the importance of linguistically motivated phonetic grouping and selective training strategies for developing robust speech enhancement models for CI applications while maintaining lightweight model complexity for edge deployment.</abstract>
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%0 Conference Proceedings
%T Leveraging Articulatory Phonetic Groupings for Robust Speech Enhancement for Cochlear Implants in Complex Acoustic Environments
%A Saha, Sohini
%A Collins, Leslie M.
%A Mainsah, Boyla
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F saha-etal-2026-leveraging
%X Cochlear implant (CI) users often experience reduced speech understanding in noisy and reverberant environments. Our recent work has shown the benefits of exploiting knowledge of speech content to drive speech enhancement for CI applications by estimating time-frequency masks based on predictions from a phoneme classifier. However, phonemes with similar temporal and spectral characteristics are likely to be confused, leading to misclassifications that negatively impact the downstream phoneme-based task. Also, machine learning models are generally vulnerable to performance degradation in unseen acoustic conditions. Using a lightweight and causal phoneme-based speech enhancement model developed for CI applications, we demonstrate the utility of alternative phoneme groups to improve model robustness in diverse acoustic settings. Results from objective measures of speech enhancement show that manner of articulation (MOA) groups provide a balance between fine-grained but error-prone phonemes and limited acoustic specificity with voicing groups. In particular, a counterintuitive finding is that MOA-based models trained in reverberant conditions are particularly robust under mismatched train-test conditions. Our findings highlight the importance of linguistically motivated phonetic grouping and selective training strategies for developing robust speech enhancement models for CI applications while maintaining lightweight model complexity for edge deployment.
%U https://aclanthology.org/2026.findings-aacl.228/
%P 4503-4529

Markdown (Informal)

[Leveraging Articulatory Phonetic Groupings for Robust Speech Enhancement for Cochlear Implants in Complex Acoustic Environments](https://aclanthology.org/2026.findings-aacl.228/) (Saha et al., Findings 2026)

• Leveraging Articulatory Phonetic Groupings for Robust Speech Enhancement for Cochlear Implants in Complex Acoustic Environments (Saha et al., Findings 2026)

ACL

• Sohini Saha, Leslie M. Collins, and Boyla Mainsah. 2026. Leveraging Articulatory Phonetic Groupings for Robust Speech Enhancement for Cochlear Implants in Complex Acoustic Environments. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 4503–4529, Hengqin, China. Association for Computational Linguistics.