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This work presents and compare two approaches for the semantic segmentation of broadcast news: the first is based on Social Network Analysis, the second is based on Poisson Stochastic Processes. Preliminary experiments address the problem of segmenting aut ...
This paper presents two approaches for speaker role recognition in multiparty audio recordings. The experiments are performed over a corpus of 96 radio bulletins corresponding to roughly 19 hours of material. Each recording involves, on average, eleven spe ...
This paper presents an approach for the segmentation of broadcast news into stories. The main novelty of this work is that the segmentation process does not take into account the content of the news, i.e. what is said, but rather the structure of the socia ...
This work presents and compare two approaches for the semantic segmentation of broadcast news: the first is based on Social Network Analysis, the second is based on Poisson Stochastic Processes. The experiments are performed over 27 hours of material: prel ...
Many daily activities present information in the form of a stream of text, and often people can benefit from additional information on the topic discussed. TV broadcast news can be treated as one such stream of text; in this paper we discuss finding news a ...
In this paper, we study the problem of content-based social network discovery among people who frequently appear in world news. Google news is used as the source of data. We describe a probabilistic framework for associating people with groups. A low-dimen ...
This work presents document clustering experiments performed over noisy texts (i.e. text that have been extracted through an automatic process like speech or character recognition). The effect of recognition errors on different clustering techniques is mea ...
This paper presents an approach for the segmentation of broadcast news into stories. The main novelty of this work is that the segmentation process does not take into account the content of the news, i.e. what is said, but rather the structure of the socia ...
This work shows how features accounting for nonverbal speaking characteristics can be used to map people into predefined categories. In particular, the results of this paper show that the speakers participating in radio broadcast news can be classified int ...
In this paper, we study the problem of content-based social network discovery among people who frequently appear in world news. Google news is used as the source of data. We describe a probabilistic framework for associating people with groups. A low-dimen ...
