Speech Recognition

TL;DR for operators The paper is not really saying “use a smaller speech model.” That would be too convenient, and reality hates convenience. It is saying something more useful: audio-model efficiency is a budget allocation problem. Model size, audio duration, encoder token resolution, and adaptation depth are different ways to spend compute, and they do not buy the same thing. Agarwal, Gangrade, Pal, and Wu study this across automatic speech recognition using Whisper on LibriSpeech and speech emotion recognition using wav2vec2 on CREMA-D.1 ...

No Easy A: Why AI Training Needs Hard-Case Routing AI teams like to say they are “improving the model.” Very noble. Also conveniently vague. In practice, “improvement” usually means one of three things: collect more data, buy a larger model, or run another round of fine-tuning and hope the loss curve behaves like a polite employee. The two papers in this cluster suggest a less glamorous, more useful idea: the scarce resource is not only data or parameters. It is learning pressure. ...

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. ...

Taxi dispatch is not a poetry recital. When a passenger calls and says, “I’m on Arguello,” the system does not need to appreciate the full expressive richness of the sentence. It needs to identify one street name, map it to the right place, and send a vehicle there. This is not a broad language-understanding task. It is a narrow operational task with coordinates attached. ...

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

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