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

Shammur Absar Chowdhury; Zien Sheikh Ali; Houssam Eddine-Othman Lachemat; Hamdy Mubarak

cs.CLOct 4, 2026

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

Authors: Shammur Absar Chowdhury, Zien Sheikh Ali, Houssam Eddine-Othman Lachemat, Hamdy Mubarak

Organizations: Qatar Computing Research Institute, Qatar

Abstract

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.

Figures & tables

Figure 1 : Overview of AraYoungVoices . Avg. Len.: average prompt length in words.

Primarily Child-Focused					Child–Adolescent / Adolescent
Corpus	Language	Age	Speakers	Hours	Corpus	Language	Age	Speakers	Hours
CHOREC [ 7 ]	NL	6–12	400	130	OGI Kids [ 15 ]	EN	K–G10	1,100	–
SingaKids [ 6 ]	ZH	7–12	255	75	PF-STAR [ 3 ]	Multi.	4–14	–	$>$ 60
ChildMandarin [ 17 ]	ZH	3–5	397	41.25	TLT-school [ 11 ]	L2 EN/DE	9–16	2,710	–
MyST [ 13 ]	EN	G3–G5	$\sim$ 1,300	$\sim$ 400	Estonian Adolescent [ 12 ]	ET	9–18	309	$\sim$ 70
BK3AT [ 10 ]	Multi.	G1–G3	–	122	Ph@ttSessionz [ 8 ]	DE	12–20	$\sim$ 1,000	–

Table 1 : Available Child and Adolescent speech corpora grouped by age coverage. K: kindergarten, G: grade.

Panel of Figure 2 — Figure 2 : Speaker characteristics in AraYoungVoices .

	7–12	13–18
Native	5.36	5.12
Non-native	8.45	6.67

Table 2 : Average utterance duration (s) by age and nativity.

Figure 3 : Char-level reading deviations in the USUP set across age groups.

#	Country	Utts. (%) / Spk.	#	Country	Utts. (%) / Spk.
1	Egypt	14,315 (16.6%) / 48	6	Morocco	5,818 (6.7%) / 17
2	India	8,081 (9.3%) / 26	7	Jordan	5,787 (6.7%) / 21
3	France	7,448 (8.6%) / 25	8	Palestine	5,485 (6.3%) / 16
4	U.S.	6,351 (7.3%) / 20	9	Pakistan	3,996 (4.6%) / 14
5	Algeria	6,165 (7.1%) / 19	10	Iraq	3,462 (4.0%) / 11

Table 3 : Top-10 countries by utterances.

Substitution	Distribution	Interpretation
ج $\rightarrow$ چ ( j $\rightarrow$ c )	L1; Egypt (94); L2; All regions (4)	Dialectal variation
ع $\rightarrow$ أ ( E $\rightarrow$ > )	L2; All regions (63)	Pharyngeal $\rightarrow$ glottal
ث $\rightarrow$ س ( v $\rightarrow$ s )	L2; Africa/Asia (31); L1; Egypt/North Africa (16)	Interdental $\rightarrow$ alveolar
ث $\rightarrow$ ت ( v $\rightarrow$ t )	L2 All reigns (23); L1; North Africa/Gulf (6)	Interdental $\rightarrow$ alveolar
ذ $\rightarrow$ د ( * $\rightarrow$ d )	L1; North Africa (3); L2; All reigns (10)	Dialectal/L2 variation
ظ $\rightarrow$ ض ( Z $\rightarrow$ D )	L1; North Africa (5)	Emphatic merger

Table 4 : Frequent consonant substitutions with linguistic interpretation; Buckwalter transliteration and frequency are shown in parentheses. چ uses extended symbol c .

Split	Kids-L1	Kids-L2	Teens-L1	Teens-L2	Total
Train	12,572 [-0.4ex] (48/17.89)	7,340 [-0.4ex] (24/16.41)	19,705 [-0.4ex] (59/27.51)	21,957 [-0.4ex] (68/41.13)	61,574 [-0.4ex] (199/102.94)
Dev	949 [-0.4ex] (3/1.58)	– [-0.4ex] (–/–)	762 [-0.4ex] (2/1.18)	– [-0.4ex] (–/–)	1,711 [-0.4ex] (5/2.75)
USUP	1,000 [-0.4ex] (26/1.57)	1,000 [-0.4ex] (40/2.43)	250 [-0.4ex] (8/0.40)	250 [-0.4ex] (8/0.38)	2,500 [-0.4ex] (82/4.78)
USSP	7,242 [-0.4ex] (26/11.40)	10,061 [-0.4ex] (40/24.36)	1,839 [-0.4ex] (8/2.97)	1,544 [-0.4ex] (8/2.52)	20,686 [-0.4ex] (82/41.25)
All	21,763 [-0.4ex] (77/32.43)	18,401 [-0.4ex] (64/43.20)	22,556 [-0.4ex] (69/32.07)	23,751 [-0.4ex] (76/44.03)	86,471 [-0.4ex] (286/151.72)

Table 5 : Corpus statistics across splits. Per split reported utterances (speakers/hrs.).

Pattern	Group	Type
ع $\rightarrow$ ا ( E $\rightarrow$ A )	L2	Pharyngeal
ق $\leftrightarrow$ ك , ق $\rightarrow$ غ ( q $\leftrightarrow$ k, q $\rightarrow$ g )	L2	Uvular
غ $\rightarrow$ ج ( g $\rightarrow$ j )	Gulf	Dialectal
ط $\rightarrow$ ت ( T $\rightarrow$ t )	Gulf	De-emphasis
ض $\rightarrow$ ظ / ز / د ( D $\rightarrow$ Z/z/d )	North Africa	Emphatic
ث $\leftrightarrow$ س / ت ( v $\leftrightarrow$ s/t )	L1	Interdental

Table 7 : Representative dialectal substitutions seen in ASR; Buckwalter transliteration is shown in parentheses.

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Arabic speech technology has largely focused on Modern Standard Arabic, leaving the living dialects spoken by hundreds of millions under-served. We introduce ALMIEYAR, a culturally grounded ASR benchmark covering 17 Arabic dialects across six families, built entirely from newly recorded speech unseen by existing models. Dialect-community coordinators selected culturally relevant images across 10 topics, and native speakers described them through five structured scenarios, yielding approximately 50 minutes per dialect (13.7 hours total). We benchmark 12 state-of-the-art ASR systems zero-shot, including GPT-4o-transcribe, Voxtral-Mini-4B, Fanar-STT-LF, Whisper, SeamlessM4T-v2, and wav2vec2-based models. GPT-4o-transcribe achieves the lowest overall WER at 35.0%, followed by Voxtral-Mini-4B, Fanar-STT-LF, and Whisper-Large-v3 at 41.1%, 45.9%, and 49.5%, respectively, indicating substantial remaining errors across Arabic dialect communities. Performance varies considerably across dialect groups, with no model performing uniformly best across all groups. WER alone also obscures dialectal ASR behaviour: wav2vec2-based models show large WER/CER gaps, where character-level agreement remains much higher than word-level accuracy, motivating joint WER/CER reporting. ALMIEYAR provides a unified benchmark for culturally grounded Arabic ASR evaluation, including the first published benchmark for Ahwazi Arabic.

Abstract

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