Paper Info
Title
Improving spontaneous children's emotion recognition by acoustic feature selection and feature-level fusion of acoustic and linguistic parameters
Abstract
This paper presents an approach to improve emotion recognition from spontaneous speech. We used a wrapper method to reduce an acoustic set of features and feature-level fusion to merge them with a set of linguistic ones. The proposed system was evaluated with the FAU Aibo Corpus. We considered the same emotion set that was proposed in the Interspeech 2009 Emotion Challenge. The main contribution of this work is the improvement, with the reduced set of features, of the results obtained in this Challenge and the combination of the best ones. We built this set with a selection of 28 acoustic and 5 linguistic features and concatenation of the feature vectors from an original set of 389 parameters.
Year
DOI
Venue
2011
10.1007/978-3-642-25020-0_12
NOLISP
Keywords
Field
DocType
linguistic feature,main contribution,emotion recognition,linguistic parameter,feature-level fusion,proposed system,emotion challenge,original set,fau aibo corpus,emotion set,acoustic feature selection,feature vector,improving spontaneous child,feature selection
Feature selection,Emotion recognition,Computer science,Fusion,AIBO,Natural language processing,Concatenation,Artificial intelligence,Merge (version control),Feature vector,Pattern recognition,Speech recognition,Linguistics
Conference
Volume
ISSN
Citations 
7015
0302-9743
0
PageRank 
References 
Authors
0.34
14
2
Name
Order
Citations
PageRank
Santiago Planet1343.24
Ignasi Iriondo292.32