Boosting bonsai trees for efficient features combination : application to speaker role identification - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Boosting bonsai trees for efficient features combination : application to speaker role identification

(1) , (2) , (3)
1
2
3

Abstract

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.
Fichier principal
Vignette du fichier
interspeech2014.pdf (237.43 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01025171 , version 1 (17-07-2014)

Identifiers

  • HAL Id : hal-01025171 , version 1

Cite

Antoine Laurent, Nathalie Camelin, Christian Raymond. Boosting bonsai trees for efficient features combination : application to speaker role identification. Interspeech, Sep 2014, Singapour, Singapore. ⟨hal-01025171⟩
635 View
512 Download

Share

Gmail Facebook Twitter LinkedIn More