InterSpeech 2021

Earnings-21: A Practical Benchmark for ASR in the Wild
(3 minutes introduction)

Miguel Del Rio (, USA), Natalie Delworth (, USA), Ryan Westerman (, USA), Michelle Huang (, USA), Nishchal Bhandari (, USA), Joseph Palakapilly (, USA), Quinten McNamara (, USA), Joshua Dong (, USA), Piotr Żelasko (Johns Hopkins University, USA), Miguel Jetté (, USA)
Commonly used speech corpora inadequately challenge academic and commercial ASR systems. In particular, speech corpora lack metadata needed for detailed analysis and WER measurement. In response, we present Earnings-21, a 39-hour corpus of earnings calls containing entity-dense speech from nine different financial sectors. This corpus is intended to benchmark ASR systems in the wild with special attention towards named entity recognition. We benchmark four commercial ASR models, two internal models built with open-source tools, and an open-source LibriSpeech model and discuss their differences in performance on Earnings-21. Using our recently released fstalign tool, we provide a candid analysis of each model’s recognition capabilities under different partitions. Our analysis finds that ASR accuracy for certain NER categories is poor, presenting a significant impediment to transcript comprehension and usage. Earnings-21 bridges academic and commercial ASR system evaluation and enables further research on entity modeling and WER on real world audio.