儿童ASR适应与成人保持：一项实证研究
原标题：Child ASR Adaptation with Adult Retention: An Empirical Study
AI 摘要
该研究针对儿童及非母语者语音识别性能不足的问题，系统比较了全量微调、LoRA 与事后权重空间合并三种策略，在阿拉伯语和英语的编码器-解码器、编码器-CTC 及 AudioLLM 类 ASR 系统上评估儿童适应与成人保持的权衡。结果显示儿童适应确有必要，但直接微调常导致成人语音性能下降；双语适应比单语适应更稳定，权重合并（LERP 偏向成人保持、TIES 偏向儿童增益）通常能改善权衡，而编码器-解码器模型上直接双语微调在原始 WER 上仍最强。研究提出 Retention Index 等指标，强调面向儿童 ASR 适应需进行保持感知评估。
正文节选
Child ASR Adaptation with Adult Retention: An Empirical Study Abstract Automatic Speech Recognition (ASR) systems often underperform for children and non-native speakers, while adapting adult ASR models to child speech can cause adult-speech forgetting. We study child ASR adaptation with adult retention across Arabic and English. We compare full fine-tuning, LoRA, and post-hoc weight-space merging across encoder--decoder, encoder--CTC, and AudioLLM-based ASR systems. Experiments use Arabic nativ