Whisper

Before the Model Speaks, Check Whether the Audio Does

TL;DR for operators A speech model can perform well when given valid speech and still behave badly on audio that should never have reached generation. Mengzhe Geng’s SURE-Voice study1 isolates that earlier decision. On the 480-example held-out SURE-Extended test, six raw speech/audio LLMs achieved only 0.000 to 0.133 accuracy on unsupported inputs. Placing the same fixed audio gate in front of each model raised unsupported accuracy to 0.919, preserved each backbone’s stored supported accuracy of 0.919 to 0.970, and reduced downstream model calls from 480 to 287. ...

Whispers Against the Noise: How Contrastive Decoding Tames Long‑Form ASR Hallucinations

A transcript is usually treated as boring infrastructure. It sits underneath meeting summaries, call-center analytics, podcast search, earnings-call review, legal discovery, medical documentation, and the cheerful dashboard that tells managers everything is now “AI-powered.” Then the transcript invents a sentence. Not a typo. Not a small mishearing. A fluent, confident, context-shaped sentence that nobody said. In short clips, this is irritating. In long recordings, it becomes structural. One bad segment can become context for the next segment; the next segment inherits the mistake; and soon the system is not transcribing a recording so much as continuing a badly seeded story. ...

When 30 Seconds Isn’t Enough: Engineering Long-Form Bangla ASR & Diarization

Call recordings are rude. They do not arrive in clean 15-second snippets. They run for minutes or hours. Speakers interrupt each other. Background noise leaks in. Someone moves away from the microphone. Someone else speaks over music, traffic, or a ceiling fan that apparently believes it deserves co-author status. This is where many speech AI demos quietly stop being impressive. ...

Whispering Feelings: When ASR Models Learn to Read Emotion

Voice systems have an awkward problem. They are getting better at hearing words, but words are not always the message. A customer says, “Fine.” A patient says, “I’m okay.” A caller says, “No problem.” The transcript is calm. The voice may not be. For call centers, mental-support triage, voice assistants, social robots, and compliance monitoring, that gap is not poetic. It is operational. ...