Entire Transcription Process

The entire transcription process encompasses converting various forms of data—speech, handwritten text, music scores, or even biological data like gene expression—into symbolic representations. Current research emphasizes improving accuracy and robustness, particularly for challenging scenarios like noisy audio, disfluent speech, low-resource languages, and complex layouts. This involves developing and refining deep learning models, including transformer architectures, recurrent neural networks, and convolutional neural networks, often coupled with techniques like transfer learning and semi-supervised training. Advances in transcription have significant implications for accessibility (e.g., captioning for the deaf and hard of hearing), language documentation, medical diagnostics, and various other fields requiring automated data processing.