Segmentation, indexing and retrieval of TV broadcast news bulletins using Gaussian mixture models and vector quantization codebooks
In this paper we proposed two-stage segmentation approach for splitting the TV broadcast news bulletins into sequence of news stories and codebooks derived from vector quantization are used for retrieving the segmented stories. At the first stage of segmentation, speaker (news reader) specific characteristics present in initial headlines of 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, and captured by Gaussian mixture models (GMMs). The proposed two-stage segmentation method is evaluated on manual segmented 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 6% are spurious. For navigating the bulletins, a quick navigation indexing method is developed based on speaker change points. Performance of the proposed two-stage segmentation and quick navigation methods are evaluated using GMM and neural networks models. For retrieving the target news stories from news corpus, sequence of codebook indices derived from vector quantization is explored. Proposed retrieval approach is evaluated using queries of different sizes. Evaluation results indicating that the retrieval accuracy is proportional to size of the query.
tv broadcast news gaussian mixture model vector quantization codebooks two-stage segmentation speaker specific information autoassociative neural network model audio indexing and retrieval