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

📰 ArXiv cs.AI

Learn how to fine-tune ASR models for low-resource children's speech recognition, improving cross-dataset, age, and gender generalization

advanced Published 19 Jun 2026

Action Steps

Apply sequence discriminative training to hybrid DNN/HMM models
Configure acoustic feature selection for different Acoustic Models
Test cross-dataset generalization using various fine-tuning strategies
Evaluate age and gender generalization performance
Compare results across different fine-tuning approaches

Who Needs to Know This

Speech recognition engineers and researchers working on low-resource languages or domains, such as children's speech, can benefit from this analysis to improve their models' performance

Key Insight

💡 Fine-tuning strategies can significantly improve cross-dataset, age, and gender generalization in low-resource children's ASR

Full Article

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

Abstract:
arXiv:2606.19791v1 Announce Type: cross Abstract: The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has demonstrated improved recognition through the use of hybrid DNN/HMM sequence discriminative training. This paper presents a comprehensive investigation of various combinations of acoustic features tailored to different Acoustic Models, offering suitable feature selection

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