Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912857120047104 |
|---|---|
| author | Sullivan, Peter Elmadany, AbdelRahim Inciarte, Alcides Alcoba Abdul-Mageed, Muhammad |
| author_facet | Sullivan, Peter Elmadany, AbdelRahim Inciarte, Alcides Alcoba Abdul-Mageed, Muhammad |
| contents | Dialectal Arabic (DA) speech data vary widely in domain coverage, dialect labeling practices, and recording conditions, complicating cross-dataset comparison and model evaluation. To characterize this landscape, we conduct a computational analysis of linguistic ``dialectness'' alongside objective proxies of audio quality on the training splits of widely used DA corpora. We find substantial heterogeneity both in acoustic conditions and in the strength and consistency of dialectal signals across datasets, underscoring the need for standardized characterization beyond coarse labels. To reduce fragmentation and support reproducible evaluation, we introduce Arab Voices, a standardized framework for DA ASR. Arab Voices provides unified access to 31 datasets spanning 14 dialects, with harmonized metadata and evaluation utilities. We further benchmark a range of recent ASR systems, establishing strong baselines for modern DA ASR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13319 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology Sullivan, Peter Elmadany, AbdelRahim Inciarte, Alcides Alcoba Abdul-Mageed, Muhammad Computation and Language Dialectal Arabic (DA) speech data vary widely in domain coverage, dialect labeling practices, and recording conditions, complicating cross-dataset comparison and model evaluation. To characterize this landscape, we conduct a computational analysis of linguistic ``dialectness'' alongside objective proxies of audio quality on the training splits of widely used DA corpora. We find substantial heterogeneity both in acoustic conditions and in the strength and consistency of dialectal signals across datasets, underscoring the need for standardized characterization beyond coarse labels. To reduce fragmentation and support reproducible evaluation, we introduce Arab Voices, a standardized framework for DA ASR. Arab Voices provides unified access to 31 datasets spanning 14 dialects, with harmonized metadata and evaluation utilities. We further benchmark a range of recent ASR systems, establishing strong baselines for modern DA ASR. |
| title | Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.13319 |