Abstract: Systems, methods, and computer-readable non-transitory storage medium in which a statistical machine translation model for formatting medical reports is trained in a learning phase using bitexts and in a tuning phase using manually transcribed dictations. Bitexts are generated from automated speech recognition dictations and corresponding formatted reports, using a series of steps including identifying matches and edits between the dictations and their corresponding reports using dynamic programming, merging matches with adjacent edits, calculating a confidence score, identifying acceptable matches, edits, and merged edits, grouping adjacent acceptable matches, edits, and merged edits, and generating a plurality of bitexts each having a predetermined maximum word count (e.g., 100 words), preferably with a predetermined overlap (e.g., two thirds) with another bitext.

Abstract: A method for assisting the transformation of a dictated, into a structured and written, report within a specialized field. The method starts with using automated speed recognition to produce a preliminary textual representation, which it then transforms into a simplified and normalized input sequence, which it copies and then transforms the copy by replacing words with tokens appropriate to the class of word as known, rare, or reducible, thereby creating a tokenized input sequence. The method then identifies and removes any preamble from the narrative text and restores punctuation, before restoring for each token within the tokenized input sequence its separable individual and original word and thus producing punctuated narrative text for processing into the written and structured report.