{"ID":2879819,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.15473","arxiv_id":"2508.15473","title":"EffortNet: A Deep Learning Framework for Objective Assessment of Speech Enhancement Technologies Using EEG-Based Alpha Oscillations","abstract":"This paper presents EffortNet, a novel deep learning framework for decoding individual listening effort from electroencephalography (EEG) during speech comprehension. Listening effort represents a significant challenge in speech-hearing research, particularly for aging populations and those with hearing impairment. We collected 64-channel EEG data from 122 participants during speech comprehension under four conditions: clean, noisy, MMSE-enhanced, and Transformer-enhanced speech. Statistical analyses confirmed that alpha oscillations (8-13 Hz) exhibited significantly higher power during noisy speech processing compared to clean or enhanced conditions, confirming their validity as objective biomarkers of listening effort. To address the substantial inter-individual variability in EEG signals, EffortNet integrates three complementary learning paradigms: self-supervised learning to leverage unlabeled data, incremental learning for progressive adaptation to individual characteristics, and transfer learning for efficient knowledge transfer to new subjects. Our experimental results demonstrate that Effort- Net achieves 80.9% classification accuracy with only 40% training data from new subjects, significantly outperforming conventional CNN (62.3%) and STAnet (61.1%) models. The probability-based metric derived from our model revealed that Transformer-enhanced speech elicited neural responses more similar to clean speech than MMSEenhanced speech. This finding contrasted with subjective intelligibility ratings but aligned with objective metrics. The proposed framework provides a practical solution for personalized assessment of hearing technologies, with implications for designing cognitive-aware speech enhancement systems.","short_abstract":"This paper presents EffortNet, a novel deep learning framework for decoding individual listening effort from electroencephalography (EEG) during speech comprehension. Listening effort represents a significant challenge in speech-hearing research, particularly for aging populations and those with hearing impairment. We...","url_abs":"https://arxiv.org/abs/2508.15473","url_pdf":"https://arxiv.org/pdf/2508.15473v1","authors":"[\"Ching-Chih Sung\",\"Cheng-Hung Hsin\",\"Yu-Anne Shiah\",\"Bo-Jyun Lin\",\"Yi-Xuan Lai\",\"Chia-Ying Lee\",\"Yu-Te Wang\",\"Borchin Su\",\"Yu Tsao\"]","published":"2025-08-21T11:49:23Z","proceeding":"eess.AS","tasks":"[\"eess.AS\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}