Automatic phonetics-driven reconstruction of medical dictations on multiple levels of segmentation

Automatic phonetic reconstruction of medical dictations from non- literal and automatically recognized speech transcripts leads to closer-to-literal transcripts for training. In this paper, we introduce an extended alignment method assessing multiple levels of text segmentation and show how open issues like wrong segmentation in the recognized transcript can be resolved. Furthermore, the effect of context-dependent reconstruction and the phonetic similarity threshold on the quality of the reconstructed transcription is measured. Experiments show an increase in precision between 0.7% and 4.7% absolute without loss in recall for the combined system incorporating all of these techniques in comparison to the system in the previous work.