  Modern automatic speech recognition (ASR) systems have achieved superhuman
Word Error Rate (WER) on many common corpora despite lacking adequate
performance on speech in the wild. Beyond that, there is a lack of real-world,
accented corpora to properly benchmark academic and commercial models. To
ensure this type of speech is represented in ASR benchmarking, we present
Earnings-22, a 125 file, 119 hour corpus of English-language earnings calls
gathered from global companies. We run a comparison across 4 commercial models
showing the variation in performance when taking country of origin into
consideration. Looking at hypothesis transcriptions, we explore errors common
to all ASR systems tested. By examining Individual Word Error Rate (IWER), we
find that key speech features impact model performance more for certain accents
than others. Earnings-22 provides a free-to-use benchmark of real-world,
accented audio to bridge academic and industrial research.
