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

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.
Type de document :
Communication dans un congrès
Interspeech, Sep 2014, Singapour, Singapore. 2014
Liste complète des métadonnées

Littérature citée [16 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01025171
Contributeur : Christian Raymond <>
Soumis le : jeudi 17 juillet 2014 - 11:32:35
Dernière modification le : mercredi 16 mai 2018 - 11:23:06
Document(s) archivé(s) le : lundi 24 novembre 2014 - 17:26:54

Fichier

interspeech2014.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01025171, version 1

Citation

Antoine Laurent, Nathalie Camelin, Christian Raymond. Boosting bonsai trees for efficient features combination : application to speaker role identification. Interspeech, Sep 2014, Singapour, Singapore. 2014. 〈hal-01025171〉

Partager

Métriques

Consultations de la notice

615

Téléchargements de fichiers

260