AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR

TL;DR

This paper introduces AfriSpeech, a comprehensive 200-hour Pan-African English speech dataset with diverse accents, aimed at advancing clinical and general domain automatic speech recognition (ASR) for African languages and addressing racial bias in speech technology.

Contribution

It provides the first large-scale, publicly available African accented speech dataset for clinical and general ASR, along with benchmark models and evaluation resources.

Findings

Achieved state-of-the-art performance on AfriSpeech benchmark

Demonstrated significant performance gaps for African accents in existing ASR systems

Provided publicly available pre-trained models for African accented speech recognition

Abstract

Africa has a very low doctor-to-patient ratio. At very busy clinics, doctors could see 30+ patients per day -- a heavy patient burden compared with developed countries -- but productivity tools such as clinical automatic speech recognition (ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous, in developed nations, and clinician-reported performance of commercial clinical ASR systems is generally satisfactory. Furthermore, the recent performance of general domain ASR is approaching human accuracy. However, several gaps exist. Several publications have highlighted racial bias with speech-to-text algorithms and performance on minority accents lags significantly. To our knowledge, there is no publicly available research or benchmark on accented African clinical ASR, and speech data is non-existent for the majority of African accents. We release AfriSpeech, 200hrs of Pan-African English speech, 67,577 clips from 2,463 unique speakers across 120 indigenous accents from 13 countries for clinical and general domain ASR, a benchmark test set, with publicly available pre-trained models with SOTA performance on the AfriSpeech benchmark.