activity
20202024
most citedEarnings-21: A Practical Benchmark for ASR in the Wild

40 citations · 62 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

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…

cs.CL2024★ 3 cited

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…

cs.CL2024★ 1 cited

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…

cs.CL2021★ 15 cited

Accented Speech Recognition: A Survey

Arthur Hinsvark, Natalie Delworth, Miguel Del Rio +9

Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in…

cs.CL2021★ 40 cited

Earnings-21: A Practical Benchmark for ASR in the Wild

Miguel Del Rio, Natalie Delworth, Ryan Westerman +7

Commonly used speech corpora inadequately challenge academic and commercial ASR systems. In particular, speech corpora lack metadata needed for detailed analysis and WER measuremen…

cs.CV2020★ 3 cited

When and how CNNs generalize to out-of-distribution category-viewpoint combinations

Spandan Madan, Timothy Henry, Jamell Dozier +6

Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent works suggest that convolutional neural networks (CNNs) fail to generalize to out-of-di…