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AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognition

AfriVox-v2 is a comprehensive multilingual speech recognition benchmark designed to evaluate ASR systems under realistic African deployment conditions. It covers 24 African languages across 10 application domains, with a strong emphasis on spontaneous, unscripted "in the wild" audio.


Dataset Summary

Most existing ASR benchmarks for African languages rely on scripted, read speech — a setting that fails to capture the complexity of real-world voice interfaces. AfriVox-v2 addresses this by aggregating conversational speech corpora, in-the-wild datasets, and providing granular domain-level annotations across sectors such as health, finance, agriculture, and government.


Languages

AfriVox-v2 covers 24 African languages spanning multiple language families:

Language Code Utterances
Nigerian Pidgin pcm 12,546
Kinyarwanda kin 12,493
Yoruba yor 10,911
Oromo orm 7,475
Igbo ibo 7,229
Amharic amh 6,317
Sesotho sot 6,317
Swahili swa 6,074
Xhosa xho 6,041
Zulu zul 5,586
Tswana tsn 5,176
Wolof wol 4,670
Fulani ful 4,098
Hausa hau 3,691
Arabic ara 3,413
Afrikaans afr 3,355
Luganda lug 2,768
Bemba bem 2,768
Pedi nso 2,455
Akan aka 1,973
Shona sna 1,185
French fra 379
Twi twi 378
Ga gaa 16

Data Sources

AfriVox-v2 aggregates three speech corpora:

Africa Next Voices (AFN)

A multi-domain decentralized African conversational speech corpus funded by the Gates Foundation. Regional grantees created and hosted data separately. For subsets with explicit test splits, those were used directly; for others, an 80/10/10 train/dev/test split was created, stratified by speaker to prevent leakage.

  • Languages: 15
  • Hours: ~100
  • Type: Conversational

Waxal

A multilingual African language speech corpus funded by Google, drawn from the full Waxal corpus.

  • Languages: 6
  • Hours: ~69.5
  • Type: Conversational

AfriVox (v1)

The original AfriVox benchmark (Awobade et al., EACL 2026), which aggregates multiple open-source corpora (NCHLT, Common Voice, FLEURS, OpenSLR, BibleTTS, NaijaVoices, FISD) alongside the novel AfriVox-Medical dataset — health-focused read speech across 20 African languages transcribed and translated by native speakers. Covers both read and medical domain speech.

  • Languages: 20
  • Hours: ~81.5 (multilingual subset)
  • Type: Read speech / Medical domain

All audio has been resampled to 16kHz.


Domain Taxonomy

AfriVox-v2 introduces a unified 10-domain taxonomy for fine-grained evaluation of real-world application performance:

Domain Sentences Duration (hrs)
General 20,117 29.67
Health 7,321 22.70
Culture & Society 4,434 7.58
Government 4,323 8.76
Finance 1,882 3.31
Education 1,793 3.74
Agriculture 947 1.38
Transportation 911 1.82
Sports & Hobbies 620 1.47
Telecommunications 470 1.31

Domain labels were generated using an LLM-assisted multilabel tagging pipeline and validated by human annotators on a random sample of ~50 utterances per language per tag, with priority given to the 6 highest-utterance languages.

In addition to the 10 standard domains, two targeted subsets are provided:

  • Named Entities (EWER): Utterances containing at least one named entity
  • Numbers (NWER): Utterances containing at least one numerical expression

Dataset Structure

Configs / Subsets

The dataset is organized into three configs corresponding to the source corpora:

Config Source Description
WAXAL Waxal corpus Conversational speech across 6 African languages
AFN Africa Next Voices Multi-domain conversational speech across 15 languages
Afrivox AfriVox v1 Read + medical domain speech across 20 languages

Fields

Each example contains the following fields:

Field Type Description
audio Audio (16kHz) Audio signal
language string ISO 639-3 language code
reference string Ground-truth transcript
domains List[string] Domain tag(s) assigned to the utterance
has_named_entities bool Whether the utterance contains named entities
has_numbers bool Whether the utterance contains numerical expressions
named_entities List[string] Named entities present in the transcript
source string Source corpus (WAXAL, AFN, or Afrivox)

Loading the Dataset

from datasets import load_dataset

# Load a specific config
ds = load_dataset("intronhealth/afrivox-v2", "AFN", split="test")

# Load all configs
from datasets import concatenate_datasets

configs = ["WAXAL", "AFN", "Afrivox"]
ds = concatenate_datasets([
    load_dataset("intronhealth/afrivox-v2", cfg, split="test")
    for cfg in configs
])

Quality Assurance

AfriVox (v1): Transcriptions and translations were produced by college-educated bilingual native speakers. A two-stage review pipeline was applied: primary transcription/translation by native speakers, followed by independent meta-review where graduate-level annotators validated 10–20% of each contributor's output. Contributors achieving less than 80% accuracy were excluded. Full details in the AfriVox paper.

AFN and Waxal: Samples with anomalously high WER from a reference model were flagged, then manually reviewed against transcripts to remove bad audio and mismatched pairs. Approximately 90% of samples were retained, confirming overall corpus quality.


Intended Use

AfriVox-v2 is intended for:

  • Benchmarking ASR models on African languages under real-world conditions
  • Domain-specific evaluation of speech systems in sectors like health, finance, and agriculture
  • Error analysis on numerically and entity-dense transcription tasks
  • Research into low-resource and underrepresented language ASR

It is not intended for model training (it is a test/evaluation benchmark).


Citation

If you use AfriVox-v2 in your research, please cite:

@article{awobade2026afrivox,
  title     = {AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognition},
  author    = {Awobade, Busayo and Ashungafac, Gabrial Zencha and Olatunji, Tobi},
  institution = {Intron Health},
  year      = {2026}
}

Contact

For questions or feedback, reach out to the Intron Health research team at research@intron.io.

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