Layer-Wise Analysis of Self-Supervised Representations for Age and Gender Classification in Children's Speech

• URL: http://arxiv.org/abs/2508.10332v1
• Date: Thu, 14 Aug 2025 04:11:44 GMT
• Title: Layer-Wise Analysis of Self-Supervised Representations for Age and Gender Classification in Children's Speech
• Authors: Abhijit Sinha, Harishankar Kumar, Mohit Joshi, Hemant Kumar Kathania, Shrikanth Narayanan, Sudarsana Reddy Kadiri,
• Abstract summary: This paper presents a detailed analysis of four Wav2Vec2 variants using the PFSTAR and CMU Kids datasets.<n>Results show that early layers capture speaker-specific cues more effectively than deeper layers.<n>Applying PCA further improves classification, reducing redundancy and highlighting the most informative components.
• Score: 27.60599947546406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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