English is the most widely spoken language in the world, used daily by
millions of people as a first or second language in many different contexts. As
a result, there are many varieties of English. Although the great many advances
in English automatic speech recognition (ASR) over the past decades, results
are usually reported based on test datasets which fail to represent the
diversity of English as spoken today around the globe. We present the first
release of The Edinburgh International Accents of English Corpus (EdAcc). This
dataset attempts to better represent the wide diversity of English,
encompassing almost 40 hours of dyadic video call conversations between
friends. Unlike other datasets, EdAcc includes a wide range of first and
second-language varieties of English and a linguistic background profile of
each speaker. Results on latest public, and commercial models show that EdAcc
highlights shortcomings of current English ASR models. The best performing
model, trained on 680 thousand hours of transcribed data, obtains an average of
19.7% word error rate (WER) -- in contrast to the 2.7% WER obtained when
evaluated on US English clean read speech. Across all models, we observe a drop
in performance on Indian, Jamaican, and Nigerian English speakers. Recordings,
linguistic backgrounds, data statement, and evaluation scripts are released on
our website (https://groups.inf.ed.ac.uk/edacc/) under CC-BY-SA license.Comment: Accepted to IEEE ICASSP 202