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Abstract

Methods for segmenting audio-video recording of meetings containing slide presentations by one or more speakers are described. These segments serve as indexes into the recorded meeting. If an agenda is provided for the meeting, these segments can be labeled using information from the agenda. The system automatically detects intervals of video that correspond to presentation slides. Under the assumption that only one person is speaking during an interval when slides are displayed in the video, possible speaker intervals are extracted from the audio soundtrack by finding these regions. Since the same speaker may talk across multiple slide intervals, the acoustic data from these intervals is clustered to yield an estimate of the number of distinct speakers and their order. Clustering the audio data from these intervals yields an estimate of the number of different speakers and their order. Merged clustered audio intervals corresponding to a single speaker are then used as training data for a speaker segmentation system. Using speaker identification techniques, the full video is then segmented into individual presentations based on the extent of each presenter's speech. The speaker identification system optionally includes the construction of a hidden Markov model trained on the audio data from each slide interval. A Viterbi assignment then segments the audio according to speaker.

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