Book ChapterDOI
A comparison using different speech parameters in the automatic emotion recognition using feature subset selection based on evolutionary algorithms
Aitor Álvarez,Idoia Cearreta,Juan Miguel López,Andoni Arruti,Elena Lazkano,Basilio Sierra,Nestor Garay +6 more
- pp 423-430
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TLDR
Using an emotional multimodal bilingual database for Spanish and Basque, emotion recognition rates in speech have significantly improved for both languages comparing with previous studies.Abstract:
Study of emotions in human-computer interaction is a growing research area. Focusing on automatic emotion recognition, work is being performed in order to achieve good results particularly in speech and facial gesture recognition. This paper presents a study where, using a wide range of speech parameters, improvement in emotion recognition rates is analyzed. Using an emotional multimodal bilingual database for Spanish and Basque, emotion recognition rates in speech have significantly improved for both languages comparing with previous studies. In this particular case, as in previous studies, machine learning techniques based on evolutive algorithms (EDA) have proven to be the best emotion recognition rate optimizers.read more
Citations
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Journal ArticleDOI
Speech emotion recognition research: an analysis of research focus
TL;DR: Analysis of research in speech emotion recognition from 2006 to 2017 finds that certain combination of databases, speech features and classifiers influence the recognition accuracy of the SER system.
Journal ArticleDOI
Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech.
TL;DR: A new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers.
Journal Article
Feature subset selection based on evolutionary algorithms for automatic emotion recognition in spoken spanish and standard basque language
Aitor Álvarez,Idoia Cearreta,Juan Miguel López,Andoni Arruti,Elena Lazkano,Basilio Sierra,Nestor Garay +6 more
TL;DR: A study performed to analyze different Machine Learning techniques validity in automatic speech emotion recognition area using a bilingual affective database and techniques based on evolutive algorithms to select speech feature subsets that optimize automatic emotion recognition success rate.
Journal ArticleDOI
Feature selection for speech emotion recognition in Spanish and Basque: on the use of machine learning to improve human-computer interaction.
TL;DR: An attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection shows that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition.
References
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Book
Affective Computing
TL;DR: Key issues in affective computing, " computing that relates to, arises from, or influences emotions", are presented and new applications are presented for computer-assisted learning, perceptual information retrieval, arts and entertainment, and human health and interaction.
Journal ArticleDOI
Emotion recognition in human-computer interaction
Roddy Cowie,Ellen Douglas-Cowie,Nicolas Tsapatsoulis,George N. Votsis,Stefanos Kollias,W. Fellenz,John G. Taylor +6 more
TL;DR: Basic issues in signal processing and analysis techniques for consolidating psychological and linguistic analyses of emotion are examined, motivated by the PKYSTA project, which aims to develop a hybrid system capable of using information from faces and voices to recognize people's emotions.
Book
Feature Selection for Knowledge Discovery and Data Mining
Huan Liu,Hiroshi Motoda +1 more
TL;DR: Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970's and provides a general framework in order to examine these methods and categorize them and suggests guidelines for how to use different methods under various circumstances.
Proceedings ArticleDOI
A survey of optimization by building and using probabilistic models
TL;DR: This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space.
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