Accessing and browsing archives of multiparty conversations can be significantly facilitated by labeling them in terms of high level information. In this paper, we investigate automatic labeling of speaker roles and topic changes in professional meetings. Using the framework of unsupervised topic modeling we express speaker utterances as mixture of latent variables, each of which is governed by a multinomial distribution. The generated latent topic distributions are then used as features for predicting role and topic changes. Experiments performed on several hours of meeting data selected from AMI corpus reveal that latent topic features are effective predictors of speaker roles and topic changes. Furthermore, experiments also reveal an improvement in performance when latent topic information is combined with other multistream features.