Whisper (2022) › The Problem
History of Language AIAll chapters Whisper (2022) › The ProblemOne section · Sections in this chapter2 / 7 ↓
Reading tools Definitions & inline links
Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.
Article links
Make inline references clickable
An ASR model can score well on the corpus it was tuned for and fail on a different microphone, accent, or speaking style. Training separate systems for every domain reduces that mismatch, but it multiplies data collection and maintenance work. The same problem becomes more expensive when each language has its own pipeline.
Labeled speech is also costly. Conventional corpora use carefully segmented recordings and checked transcripts. They provide clean supervision but cover only a small part of the audio found in actual use. Low-resource languages receive less coverage because suitable recordings and transcripts are scarce.
Speech translation added another pipeline. A system might first transcribe the source language and then pass the text to a translation model. Errors from the first stage become input to the second. Whisper instead learned direct speech-to-English translation in the same sequence model used for transcription.