---
pretty_name: Voices for Smart Care
license: cc-by-nc-sa-4.0
language:
  - ha
  - yo
  - ig
  - ff
  - kr
  - pcm
  - nup
task_categories:
  - automatic-speech-recognition
  - translation
tags:
  - speech
  - healthcare
  - maternal-health
  - multilingual
  - nigeria
  - low-resource
  - asr
  - speech-to-text
  - translation
  - evaluation
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: language
      dtype: string
    - name: text
      dtype: string
    - name: duration
      dtype: float
  splits:
    - name: train
      num_bytes: 109338207654.348
      num_examples: 5323
  download_size: 69061495991
  dataset_size: 109338207654.348
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
---

# Voices for Smart Care

## A Rural-First Multilingual Voice Dataset for Maternal Health in Nigeria

Voices for Smart Care is a multilingual speech dataset containing real-world maternal and reproductive health questions collected from women across Nigeria. The dataset was created to support the development and evaluation of Automatic Speech Recognition (ASR), Machine Translation (MT), and Large Language Models (LLMs) for low-resource African languages in healthcare settings.

Unlike generic speech datasets, Voices for Smart Care focuses on natural health-related conversations collected from the target population—particularly women living in rural and peri-urban communities—capturing realistic accents, dialects, code-switching, spontaneous speech, and environmental noise.

This release contains **150.66 hours** across **5,323 recordings**, and represents a subset of the planned 600-hour corpus (100 hours each for Hausa, Igbo, Yoruba, Fulfulde, and Nigerian Pidgin; 50 hours each for Nupe and Kanuri). Language coverage is intentionally imbalanced at this stage to reflect data available at time of release.

---

# Dataset Summary

The dataset contains audio recordings together with manually verified transcriptions in seven Nigerian languages.

**Features**

| Field | Type | Description |
|---|---|---|
| `audio` | `audio` | Speech recording |
| `text` | `string` | Human transcription |
| `language` | `string` | Language identifier |
| `duration` | `float` | audio duration in seconds |

The dataset is intended for:

- Automatic Speech Recognition (ASR)
- Speech model evaluation
- Speech translation research
- Domain adaptation
- Healthcare NLP
- Low-resource language modeling

---

# Languages

The dataset covers seven Nigerian languages.

| Language | Hours | Recordings |
|----------|------:|-----------:|
| Hausa | 51.85 | 1,559 |
| Yoruba | 51.84 | 2,074 |
| Nigerian Pidgin | 25.95 | 1,026 |
| Nupe | 8.45 | 257 |
| Igbo | 4.40 | 117 |
| Kanuri | 4.37 | 154 |
| Fulfulde | 3.80 | 136 |
| **Total** | **150.66** | **5,323** |

---

# Data Collection

The Smart Care initiative aims to enable women across Nigeria to access maternal and reproductive healthcare information using voice interfaces in their native languages.

Data collection prioritized:

- Rural communities
- Peri-urban communities
- Women with recent maternal healthcare experience
- Natural spoken health questions
- Realistic recording environments

Speech was collected through multiple channels including:

- Community health centres
- Rural clinics
- WhatsApp voice recordings
- Assisted recordings by trained field researchers

Rather than reading scripted prompts, participants were encouraged to describe authentic maternal health concerns in their own words.

---

# Collection Methodology

The dataset was designed using an error-driven data collection strategy.

## 1. Error Analysis

Existing multilingual maternal-health speech datasets were first used to benchmark current ASR systems and identify weaknesses such as:

- medical terminology
- symptoms
- medication names
- pregnancy timelines
- numerical expressions
- dialect variation
- environmental robustness

These findings informed the design of subsequent data collection.

## 2. Population-Matched Recruitment

Recruitment focused primarily on rural and peri-urban women to better reflect the intended users of Smart Care voice assistants.

## 3. Natural Speech

Participants described maternal and reproductive health situations using their own language rather than reading scripted sentences.

Typical queries include:

- pregnancy symptoms
- antenatal care
- newborn care
- breastfeeding
- nutrition
- malaria
- fever
- labour
- postpartum recovery
- medication questions

## 4. Quality Assurance

Each recording underwent:

- manual transcription
- language verification
- transcription quality review

---

# Intended Uses

The dataset is intended for research on:

- Automatic Speech Recognition
- Speech Foundation Models
- Domain Adaptation
- Healthcare AI
- Speech Translation
- Machine Translation
- Large Language Models
- Retrieval-Augmented Generation
- Benchmarking multilingual healthcare systems

---

# Example

```python
from datasets import load_dataset

dataset = load_dataset("intronhealth/Nig-Voice-Health-Bench")

sample = dataset["train"][0]

print(sample["audio"])
print(sample["text"])
print(sample["language"])
```

---

<!-- # Citation

A formal citation will be added upon publication. In the meantime, if you use this dataset, please reference it as:

```bibtex
@dataset{voices_for_smart_care,
  title={Voices for Smart Care: A Rural-First Multilingual Voice Dataset for Maternal Health in Nigeria},
  year={2026}
}
``` -->

---

# Acknowledgements

This dataset was developed as part of the **Smart Care** initiative to improve equitable access to maternal healthcare information through multilingual voice technologies.

We gratefully acknowledge the contributions of participating women, community health workers, healthcare facilities, field researchers, transcribers, and language experts who made this resource possible.

---

# Limitations

- The dataset focuses on maternal and reproductive health and should not be considered representative of general conversational speech.
- Language coverage is intentionally imbalanced to reflect available data at the time of release.
- Recordings include natural environmental noise, accents, and spontaneous speech, which may increase task difficulty but better reflect real-world deployment conditions.
- This release is a partial (~150-hour) subset of the planned 600-hour corpus and should not be treated as the final or complete collection.

---

# Ethical Considerations

Data collection was conducted with informed participant consent. Personally identifying information was removed during processing. The dataset is intended solely for research and development of equitable healthcare technologies and should not be used to infer sensitive personal attributes about speakers.

---

# License

This dataset is released under the **CC BY-NC-SA 4.0** license.