15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

Boosting Bonsai Trees for Efficient Features Combination: Application to Speaker Role Identification

Antoine Laurent (1), Nathalie Camelin (2), Christian Raymond (3)

(1) LIMSI, France
(2) LIUM, France
(3) IRISA, France

In this article, we tackle the problem of speaker role detection from broadcast news shows. In the literature, many proposed solutions are based on the combination of various features coming from acoustic, lexical and semantic information with a machine learning algorithm. Many previous studies mention the use of boosting over decision stumps to combine efficiently these features. In this work, we propose a modification of this state-of-the-art machine learning algorithm changing the weak learner (decision stumps) by small decision trees, denoted bonsai trees. Experiments show that using bonsai trees as weak learners for the boosting algorithm largely improves both system error rate and learning time.

Full Paper

Bibliographic reference.  Laurent, Antoine / Camelin, Nathalie / Raymond, Christian (2014): "Boosting bonsai trees for efficient features combination: application to speaker role identification", In INTERSPEECH-2014, 76-80.