arXiv cs.CL· Houssam Eddine-Othman Lachemat, Shammur Absar Chowdhury·· 10 天前AI 评分27
儿童 ASR 适配与成人语音保持：一项实证研究
Child ASR Adaptation with Adult Retention: An Empirical Study
AI 导读
研究比较了全量微调、LoRA 与事后权重空间合并在阿拉伯语和英语儿童 ASR 适配中的表现，覆盖 encoder-decoder、encoder-CTC 和 AudioLLM 三类系统。结果显示儿童适配确有必要，但直接适配常导致成人语音识别性能下降；权重空间合并（LERP 偏向成人保持、TIES 恢复更强儿童增益）往往能改善这一权衡，双语适配比单语适配更稳定。
正文 · 原文
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 native and non-native child speech, English MyST child speech, and adult benchmarks from MGB-2 and LibriSpeech test-clean. We evaluate recognition quality with WER and quantify the adaptation--retention trade-off using Retention Index, Child Adaptation Gain, and Adaptation Recovery. Results show that child adaptation is necessary, especially for non-native Arabic and English child speech, but direct adaptation often reduces adult ASR performance. Bilingual adaptation is more stable than language-specific adaptation. Weight-space merging often improves the trade-off, especially for encoder--CTC, Whisper, and AudioLLM-based ASR, with LERP favoring adult retention and TIES recovering stronger child gains. For the encoder--decoder model, direct bilingual fine-tuning remains strongest in raw WER.\footnote{Code, and models are available at this https URL.
| Comments: | long paper |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| MSC classes: | 68T50 |
| ACM classes: | F.2.2; I.2.7 |
| Cite as: | arXiv:2610.08827 [cs.CL] |
| (or arXiv:2610.08827v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08827 arXiv-issued DOI via DataCite |
Submission history
From: Houssam Eddine-Othman Lachemat [view email]
[v1]
Fri, 25 Sep 2026 18:10:54 UTC (651 KB)
来源：arXiv cs.CL · arxiv.org