Role Recognition in Broadcast News using Bernoulli Distributions

This work presents an approach for the recognition of the roles played by speakers participating in radio broadcast news (e.g. anchorman or guest). The approach includes two main stages: the first is the split of the news recordings into single speaker segments using an unsupervised approach. The second is the application of Bernoulli Distributions for role modeling and recognition. The experiments are performed over a collection of 96 news bulletins (around 19 hours of material) and show that around 80 percent of the data time is labeled correctly in terms of role.