Automatic Transcription

Automatic transcription aims to convert spoken language into written text, a task crucial for accessibility and data analysis across diverse domains. Current research focuses on improving accuracy and robustness of transcription models, particularly for challenging audio like multilingual speech, noisy recordings, and conversational or informal language, often employing deep learning architectures such as transformers and recurrent neural networks. These advancements are impacting various fields, from archival research and medical record analysis to enhancing accessibility of audio-visual content and facilitating large-scale linguistic studies. The development of robust and accurate transcription systems is driving progress in related areas like machine translation and information extraction.