Captions are an essential part of using video for teaching and learning. While Automatic Speech Recognition (ASR) has made significant advances recently, there are still issues with accuracy and context in transcripts generated by ASR, particularly in fields with specific terminology.
Human correction of ASR transcripts or human transcription is the way to achieve 100% accuracy, but comes at a monetary and time cost that is not feasible for most teachers or learning institutions.
This study seeks to understand the current state of widely-available ASR transcript generation tools by analyzing transcripts generated by these systems with known 100% accurate transcripts. An additional goal of the study is to identify possible user decisions/practices or equipment choices/use that affect ASR transcript quality for future study.
Project Team: Scott Schopieray, Betsy Sneller, Kate Sonka, Daniel Trego