# AraYoungVoices: A Diverse L1/L2 Corpus of Arabic Child and Adolescent Speech

arXiv: https://arxiv.org/abs/2610.05044 · HTML: https://mlsanity.com/paper/2610.05044/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 4 Oct 2026.

## Builds on (most cited first, 6 of 6)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [My Science Tutor (MyST) -- A Large Corpus of Children's Conversational Speech](https://mlsanity.com/paper/2309.13347/) (2023)
- [TLT-school: a Corpus of Non Native Children Speech](https://mlsanity.com/paper/2001.08051/) (2020)
- [ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5](https://mlsanity.com/paper/2409.18584/) (2024)
- [Arabic Little STT: Arabic Children Speech Recognition Dataset](https://mlsanity.com/paper/2510.23319/) (2025)
- [Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs](https://mlsanity.com/paper/2601.12494/) (2026)

## Abstract (arXiv)

State-of-the-art ASR systems primarily target native adult speech, leading to substantial performance gaps for children, adolescents, and L2 speakers. We introduce AraYoungVoices, a 151.72-hour Arabic read-speech corpus from 286 speakers aged 7--18, comprising AraKids (7--12) and AraTeens (13--18). The corpus includes 146 native Arabic (L1) and 140 second-language (L2) speakers, with native speakers spanning Egyptian, Gulf, Levantine, and North African dialectal backgrounds and L2 speakers representing diverse linguistic backgrounds across the Americas, Asia, Africa, and Europe. We benchmark four pretrained ASR models under zero-shot and fine-tuned settings using unseen-speaker-&-unseen-prompt (USUP) and unseen-speaker-&-seen-prompt (USSP) evaluations. Results show that L2 speech remains substantially more challenging than L1 speech, with the largest errors observed mainly for younger L2 speakers. Age-specific fine-tuning improves the matched age group, while joint fine-tuning provides a stronger balance across populations. ASR hypotheses are also consistently closer to the standard reading prompt than to the verbatim transcription, particularly for L2 speech, suggesting partial normalization of reading deviations.