{"ID":2882146,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10332","arxiv_id":"2508.10332","title":"Layer-Wise Analysis of Self-Supervised Representations for Age and Gender Classification in Children's Speech","abstract":"Children's speech presents challenges for age and gender classification due to high variability in pitch, articulation, and developmental traits. While self-supervised learning (SSL) models perform well on adult speech tasks, their ability to encode speaker traits in children remains underexplored. This paper presents a detailed layer-wise analysis of four Wav2Vec2 variants using the PFSTAR and CMU Kids datasets. Results show that early layers (1-7) capture speaker-specific cues more effectively than deeper layers, which increasingly focus on linguistic information. Applying PCA further improves classification, reducing redundancy and highlighting the most informative components. The Wav2Vec2-large-lv60 model achieves 97.14% (age) and 98.20% (gender) on CMU Kids; base-100h and large-lv60 models reach 86.05% and 95.00% on PFSTAR. These results reveal how speaker traits are structured across SSL model depth and support more targeted, adaptive strategies for child-aware speech interfaces.","short_abstract":"Children's speech presents challenges for age and gender classification due to high variability in pitch, articulation, and developmental traits. While self-supervised learning (SSL) models perform well on adult speech tasks, their ability to encode speaker traits in children remains underexplored. This paper presents...","url_abs":"https://arxiv.org/abs/2508.10332","url_pdf":"https://arxiv.org/pdf/2508.10332v1","authors":"[\"Abhijit Sinha\",\"Harishankar Kumar\",\"Mohit Joshi\",\"Hemant Kumar Kathania\",\"Shrikanth Narayanan\",\"Sudarsana Reddy Kadiri\"]","published":"2025-08-14T04:11:44Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.AI\",\"cs.HC\",\"cs.LG\",\"cs.SD\"]","methods":"[]","has_code":false}