most citedCan Layer-wise SSL Features Improve Zero-Shot ASR Performance for Children's Speech?

4 citations · 7 across the 3 of their papers we have counts for

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eess.AS2026

Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR

Abhijit Sinha, Hemant Kumar Kathania, Sudarsana Reddy Kadiri +1

The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has d…

eess.AS2026

How Well Do Self-Supervised Speech Models Encode Age and Gender in Children's Speech? A Layer-Wise Analysis Across Multiple Architectures

Abhijit Sinha, Hemant Kumar Kathania, Mohit Joshi +3

Self-supervised learning (SSL) models have become a central component of modern speech processing systems, as they enable the learning of rich acoustic representations without reli…

eess.AS20253 cited

Zero-Shot KWS for Children's Speech using Layer-Wise Features from SSL Models

Subham Kutum, Abhijit Sinha, Hemant Kumar Kathania +2

Numerous methods have been proposed to enhance Keyword Spotting (KWS) in adult speech, but children's speech presents unique challenges for KWS systems due to its distinct acoustic…

eess.AS20254 cited

Can Layer-wise SSL Features Improve Zero-Shot ASR Performance for Children's Speech?

Abhijit Sinha, Hemant Kumar Kathania, Sudarsana Reddy Kadiri +1

Automatic Speech Recognition (ASR) systems often struggle to accurately process children's speech due to its distinct and highly variable acoustic and linguistic characteristics. W…

eess.AS2025

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

Abhijit Sinha, Harishankar Kumar, Mohit Joshi +3

Children's speech presents challenges for age and gender classification due to high variability in pitch, articulation, and developmental traits. While self-supervised learning (SS…