Researchers have explored methods for adapting Automatic Speech Recognition (ASR) systems to better understand child speech while retaining performance on adult speech. The study compared techniques like full fine-tuning, LoRA, and weight-space merging across different ASR architectures, including encoder-decoder, encoder-CTC, and AudioLLM-based systems. Experiments focused on Arabic and English child speech, revealing that while adaptation is crucial, direct methods can degrade adult speech recognition. Weight-space merging techniques often provided a better balance between child adaptation and adult retention, particularly for certain ASR architectures. AI
IMPACT This research could lead to more robust ASR systems capable of accurately transcribing both child and adult speech, improving accessibility and usability for diverse user groups.
RANK_REASON Academic paper detailing empirical study of ASR adaptation techniques. [lever_c_demoted from research: ic=1 ai=1.0]
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