Towards a Small Set of Robust Acoustic Features for Emotion Recognition: Challenges

Abstract : The search of a small acoustic feature set for emotion recognition faces three main challenges. Such a feature set must be robust to large diversity of contexts in real-life applications; model parameters must also be optimized for reduced subsets; finally, the result of feature selection must be evaluated in cross-corpus condition. The goal of the present study is to select a consensual set of acoustic features for valence recognition using classification and non-classification based feature ranking and cross-corpus experiments, and to optimize emotional models simultaneously. Five realistic corpora are used in this study: three of them were collected in the framework of the French project on robotics ROMEO, one is a game corpus (JEMO) and one is the well-known AIBO corpus. Combinations of features found with non-classification based methods (information gain and Gaussian mixture models with Bhattacharyya distance) through multi-corpora experiments are tested under cross-corpus conditions, simultaneously with SVM parameters optimization. Reducing the number of features goes in pair with optimizing model parameters. Experiments carried on randomly selected features from two acoustic feature sets show that a feature space reduction is needed to avoid over-fitting. Since a Grid search tends to find non-standard values with small feature sets, the authors propose a multi-corpus optimization method based on different corpora and acoustic feature subsets which ensures more stability. The results show that acoustic families selected with both feature ranking methods are not relevant in cross-corpus experiments. Promising results have been obtained with a small set of 24 voiced cepstral coefficients while this family was ranked in the 2nd and 5th positions with both ranking methods. The proposed optimization method is more robust than the usual Grid search for cross-corpus experiments with small feature sets.
Type de document :
Article dans une revue
IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2016, IEEE/ACM Transactions on Audio, Speech and Language Processing, 24, pp.16 - 28. 〈10.1109/TASLP.2015.2487051〉
Liste complète des métadonnées

Littérature citée [80 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01404146
Contributeur : Marie Tahon <>
Soumis le : lundi 28 novembre 2016 - 17:39:49
Dernière modification le : jeudi 11 janvier 2018 - 06:25:47
Document(s) archivé(s) le : mardi 21 mars 2017 - 06:56:01

Fichier

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

Identifiants

Collections

Citation

Marie Tahon, Laurence Devillers. Towards a Small Set of Robust Acoustic Features for Emotion Recognition: Challenges. IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2016, IEEE/ACM Transactions on Audio, Speech and Language Processing, 24, pp.16 - 28. 〈10.1109/TASLP.2015.2487051〉. 〈hal-01404146〉

Partager

Métriques

Consultations de la notice

111

Téléchargements de fichiers

179