Ulteriori informazioni
This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications - gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
Sommario
Introduction.- Emotion Recognition using Prosodic features.- Emotion Recognition using Spectral features.- Emotional Speech Corpora.- Classification Models.- Comparative Analysis of Classifiers in emotion recognition.- Summary and Conclusions.
Riassunto
This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
Testo aggiuntivo
“The aim of this book is to bring out various features through speech processing, and use them in an acoustic model to recognize the emotion conveyed by the person. … the monogram looks concise and interesting and should be of interest to postgraduates and researchers in speech processing.” (Soubhik Chakraborty, Computing Reviews, April, 2016)
Relazione
"The aim of this book is to bring out various features through speech processing, and use them in an acoustic model to recognize the emotion conveyed by the person. ... the monogram looks concise and interesting and should be of interest to postgraduates and researchers in speech processing." (Soubhik Chakraborty, Computing Reviews, April, 2016)
