Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Mariana Julião (Autor) FEUP; Jorge Silva (Autor) Outra; Ana Aguiar (Autor) FEUP; Helena Moniz (Autor) Outra; Fernando Baptista (Autor) Outra

Ata de Conferência Internacional

Título: LANGUAGES, APPLICATIONS AND TECHNOLOGIES, SLATE 2015

Páginas: 3-14

4th International Symposium on Languages, Applications and Technologies (SLATE)

Complutense Univ Madrid, Fac Philol, Madrid, SPAIN, JUN 18-19, 2015

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00K-2XV

DOI: 10.1007/978-3-319-27653-3_1

Abstract (EN): Stress detection from speech is a less explored field than Automatic Emotion Recognition and it is still not clear which features are better stress discriminants. The project VOCE aims at doing speech classification as stressed or not-stressed in real-time, using acoustic-prosodic features only. We therefore look for the best discriminating feature subsets from a set of 6125 features extracted with openSMILE toolkit plus 160 Teager Energy Operator (TEO) features. We use a Mutual Information (MI) filter and a branch and bound wrapper heuristic with an SVM classifier to perform feature selection. Since many feature sets are selected, we analyse them in terms of chosen features and classifier performance concerning also true positive and false positive rates. The results show that the best feature types for our application case are Audio Spectral, MFCC, PCM and TEO. We reached results as high as 70.4% for generalisation accuracy.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12