Automatic content segmentation of audio recordings at multidisciplinary medical team meetings

A single recording of a multidisciplinary medical team meeting (MDTM) can be expected to contain several separate discussions on different patients. Automatic speaker segmentation alone does not allow for the separation of individual patient case discussions (PCDs). A novel method is presented here, based on Hidden Markov Models (HMM), to segment audio recordings of MDTMs and facilitate the non-linear retrieval of individual PCDs. The method combines professional role interaction with speaker vocalization patterns. The sequence and duration of vocalization and speakers' roles are used as training states. Results demonstrate HMM segmentation to have good potential in the development of an MDTM browser. The approach outlined here can be applied in a wide range of meetings.