Typically, the output of ASR is a mere sequence of words. This view may be sufficient for some tasks, whereas others require a more structured approach. This thesis presents a framework that allows for identification of deep, underlying structure in report dictations.
Identification of structural elements, such as headings, sections and enumerations is an important step towards automatic post-processing of dictated speech. The contributions of this thesis include a generic approach that can be integrated seamlessly with existing ASR solutions and provides structured output, as well as a freely available CRF toolkit that forms the basis of aforementioned approach and may also be applicable to numerous other problems.