4 citations · 8 across the 6 of their papers we have counts for
6 papers
Beyond Single Ground Truth: Reference Monism as Epistemic Injustice in ASR Evaluation
Anna Seo Gyeong Choi, Maria Teleki, James Caverlee +3
Automatic speech recognition (ASR) evaluation compares system output to ground truth transcripts, with Word Error Rate (WER) quantifying the distance between them. But ground truth…
Style-agnostic evaluation of ASR using multiple reference transcripts
Quinten McNamara, Miguel Ángel del Río Fernández, Nishchal Bhandari +4
Word error rate (WER) as a metric has a variety of limitations that have plagued the field of speech recognition. Evaluation datasets suffer from varying style, formality, and inhe…
Reverb: Open-Source ASR and Diarization from Rev
Nishchal Bhandari, Danny Chen, Miguel Ángel del Río Fernández +10
Today, we are open-sourcing our core speech recognition and diarization models for non-commercial use. We are releasing both a full production pipeline for developers as well as pa…
Quantification of stylistic differences in human- and ASR-produced transcripts of African American English
Annika Heuser, Tyler Kendall, Miguel del Rio +4
Common measures of accuracy used to assess the performance of automatic speech recognition (ASR) systems, as well as human transcribers, conflate multiple sources of error. Stylist…
Updated Corpora and Benchmarks for Long-Form Speech Recognition
Jennifer Drexler Fox, Desh Raj, Natalie Delworth +3
The vast majority of ASR research uses corpora in which both the training and test data have been pre-segmented into utterances. In most real-word ASR use-cases, however, test audi…
Earnings-22: A Practical Benchmark for Accents in the Wild
Miguel Del Rio, Peter Ha, Quinten McNamara +2
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.…