metadata

license: cc-by-nc-sa-4.0
task_categories:
  - automatic-speech-recognition
language:
  - en
tags:
  - medical
  - africa

AfriSpeech-Dialog v1: A Conversational Speech Dataset for African Accents

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Overview and Purpose

AfriSpeech-Dialog is a pan-African conversational speech dataset with 6 hours of recorded dialogue, designed to support speech recognition (ASR) and speaker diarization applications. Collected from diverse accents across Nigeria, Kenya, and South Africa, the dataset offers valuable insights into the varied linguistic and phonetic characteristics found in African-accented English. This release includes 50 conversations across both medical and general topics.

Dataset Statistics

	Medical	General
Counts	20	29
Timestamped Counts	9	21
Avg. Num. of Turns	78.6	30.55
Total Duration (hrs)	2.07	4.93
Avg. Word Count	725.3	1356.83
Num. of Countries	1	3
Num. of Accents	6	8
Genders (M, F)	(14,26)	(25,33)

Use Cases

This dataset is tailored for use in:

Automatic Speech Recognition (ASR) fine-tuning
Speaker Diarization training and testing

Dataset Composition

Languages and Accents: The dataset includes 11 accents: Hausa, Isoko, Idoma, Urhobo, Ijaw, Yoruba, Swahili, Sesotho, Igbo, Igala, and Ebira.
Domains: Conversations span two domains—20 medical conversations, simulating doctor-patient interactions, and 30 general-topic conversations.
Participants: The dataset includes both male and female speakers.
Structure of Conversations: Conversations are two-speaker free-form dialogues.

Data Collection and Processing

Collection Method: Conversations were collected remotely across various acoustic environments as stored as .wav files.
Annotation: Each conversation is annotated with speaker labels and timestamps, including start and end times for each speaker’s turn.

Key Columns and Fields

file_name: Path to the audio file.
transcript: Full transcript of the conversation with timestamps.
domain: Indicates the conversation type, either medical or general.
duration: Duration of the audio file, in seconds.
age_group: Age group of the speakers.
accent: Primary accent represented in the conversation.
country: Country of origin for the speakers.

Usage Instructions

Accessing the Dataset: The dataset can be accessed through Hugging Face:

from datasets import load_dataset
afrispeech_dialog = load_dataset("intronhealth/afrispeech-dialog")