ASR

Talk Is Cheap, Until It Trains ASR Call centers are very good at producing audio. They are much worse at producing clean, labeled, domain-matched, multi-speaker training data. That distinction matters. A business may have thousands of hours of customer calls, branch conversations, medical consultations, field-service recordings, or internal support audio. But most of it is noisy, consent-constrained, poorly transcribed, unevenly distributed across accents and topics, and inconveniently full of humans doing human things: interrupting, pausing, talking over each other, drifting off-topic, and using domain-specific shorthand as if the ASR model had attended the onboarding session. ...

Voice is supposed to be the easy interface. No menus. No forms. No training session. A user speaks, the agent understands, and some neat piece of software magic happens in the background. That is the sales pitch. It is also mostly true in a demo room, which is a place where microphones behave, users speak politely, and nobody’s child interrupts from the back seat. ...

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. ...

IBM’s open-weight large speech model trained for high-quality multilingual automatic speech recognition (ASR) and transcription.

A widely used self-supervised speech representation model from Meta AI for automatic speech recognition and audio understanding tasks.

A multilingual speech recognition and translation model by OpenAI, supporting 100+ languages with improved robustness and low-latency transcription.