AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and
  General Domain ASR
Tobi Olatunji Tejumade Afonja Aditya Yadavalli Chris Chinenye Emezue Sahib Singh Bonaventure F. P. Dossou Joanne Osuchukwu Salomey Osei Atnafu Lambebo Tonja Naome Etori Clinton Mbataku
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.