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[null,"1.","Record Nr.",null,"UNINA9910299699903321"]
[null,null,"Autore",null,"Anne Koteswara Rao"]
[null,null,"Titolo",null,"Acoustic Modeling for Emotion Recognition / / by Koteswara Rao Anne, Swarna Kuchibhotla, Hima Deepthi Vankayalapati"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015"]
[null,null,"ISBN",null,"3-319-15530-X"]
[null,null,"Edizione",null,"[1st ed. 2015.]"]
[null,null,"Descrizione fisica",null,"1 online resource (72 p.)"]
[null,null,"Collana",null,"SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning, , 2191-737X"]
[null,null,"Disciplina",null,"621.382"]
[null,null,"Soggetti",null,"Signal processing"]
[{"$nulls":4},"Image processing"]
[{"$nulls":4},"Speech processing systems"]
[{"$nulls":4},"Computational linguistics"]
[{"$nulls":4},"User interfaces (Computer systems)"]
[{"$nulls":4},"Acoustics"]
[{"$nulls":4},"Signal, Image and Speech Processing"]
[{"$nulls":4},"Computational Linguistics"]
[{"$nulls":4},"User Interfaces and Human Computer Interaction"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"Sommario/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."]