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KIRA 是一个面向儿童早期阅读筛查的全栈系统，结合词级流利度评分与音素级错误检测的双通路分析，将儿童语音识别的词错误率从 13.88% 降至 8.84%、音素错误率从 41.66% 降至 23.00%，并配套教师端 Web 界面以支持课堂部署与针对性干预。
An empirical study of adapting adult ASR models to child speech across Arabic and English while retaining adult performance, comparing fine-tuning, LoRA, and weight-space merging (LERP/TIES), with findings that bilingual adaptation and merging improve the adaptation-retention trade-off.
This paper introduces a method using GPT-2 token surprisal to label telicity in child language, finding that child models rely on syntactic cues like post-verbal determiners, while adult models use semantic features, supporting syntactic bootstrapping theory.
This paper evaluates nine ASR models (Whisper, Parakeet, Wav2Vec2) on Dutch child speech datasets JASMIN and DART, finding that fine-tuned Whisper-medium achieves the best performance (WER 5.54% on JASMIN, 70.37% on DART). It also proposes a selection method to automatically identify correctly pronounced utterances with high precision, reducing the need for manual verification.