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Researchers Analyze Fine-Tuning Strategies for Children's Speech Recognition Across Age, Gender, and Datasets

A new study from researchers including Sapkota and Narayanan provides a comprehensive analysis of fine-tuning strategies for children's automatic speech recognition (ASR), focusing on cross-dataset, age, and gender generalization. Using the TORGO database, they achieved a 4.65% relative improvement in isolated word recognition and 4.63% in sentence recognition for dysarthric speech by employing a Factorized Time Delay Neural Network (F-TDNN) with pitch features.