Segmentation of TV broadcast news using speaker specific information

In this paper, we proposed two-stage segmentation approach for splitting the TV broadcast news bulletins into sequence of news stories. In the first stage, speaker (news reader) specific characteristics present in initial headlines of the news bulletin are used for gross level segmentation. During second stage, errors in the gross level segmentation (first stage) are corrected by exploiting the speaker specific information captured from the individual news stories other than headlines. During headlines the captured speaker specific information is mixed with background music, and hence the segmentation at the first stage may not be accurate. In this work speaker specific information is represented by using mel frequency cepstral coefficients (MFCCs), and it is captured by using Gaussian mixture models (GMMs). The proposed two-stage segmentation method is evaluated on manual segmented ten broadcast TV news bulletins. From the evaluation results, it is observed that about 93% of the news stories are correctly segmented, 7% are missed and 11% are spurious.