Transcription Error

Transcription error, the inaccuracy in converting spoken language to written text, is a significant challenge across various applications, from medical record-keeping to language learning software. Current research focuses on improving automatic speech recognition (ASR) systems through techniques like optimized tokenization and the development of error correction models, often leveraging synthetic data or incorporating phoneme information to enhance accuracy. These efforts aim to reduce error rates and improve the reliability of transcribed data, impacting fields reliant on accurate speech-to-text conversion, such as healthcare and machine translation, where even small errors can have substantial consequences.