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[null,"1.","Record Nr.",null,"UNINA9910437893803321"]
[null,null,"Autore",null,"Krothapalli Sreenivasa Rao"]
[null,null,"Titolo",null,"Emotion recognition using speech features / / Sreenivasa Rao Krothapalli, Shashidhar G. Koolagudi"]
[null,null,"Pubbl/distr/stampa",null,"New York, : Springer, 2013"]
[null,null,"ISBN",null,"9781283908764"]
[{"$nulls":4},"128390876X"]
[{"$nulls":4},"9781461451433"]
[{"$nulls":4},"1461451434"]
[null,null,"Edizione",null,"[1st ed. 2013.]"]
[null,null,"Descrizione fisica",null,"1 online resource (133 p.)"]
[null,null,"Collana",null,"SpringerBriefs in electrical and computer engineering : SpringerBriefs in speech technology, , 2191-8112"]
[null,null,"Disciplina",null,"006.454"]
[null,null,"Soggetti",null,"Emotions"]
[{"$nulls":4},"Speech perception"]
[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 -- Speech Emotion Recognition: A Review -- Emotion Recognition Using Excitation Source Information -- Emotion Recognition Using Vocal Tract Information -- Emotion Recognition Using Prosodic Information -- Summary and Conclusions -- Linear Prediction Analysis of Speech -- MFCC Features -- Gaussian Mixture Model (GMM)."]
[null,null,null,"Sommario/riassunto","“Emotion Recognition Using Speech Features” covers emotion-specific features present in speech and discussion of suitable models for capturing emotion-specific information for distinguishing different emotions.  The content of this book is important for designing and developing  natural and sophisticated speech systems. Drs. Rao and Koolagudi lead a discussion of how emotion-specific information is embedded in speech and how to acquire emotion-specific knowledge using appropriate statistical models. Additionally, the authors provide information about using evidence derived from various features and models. The acquired emotion-specific knowledge is useful for synthesizing emotions. Discussion includes global and local prosodic features at syllable, word and phrase levels, helpful for capturing emotion-discriminative information; use of complementary evidences obtained from excitation sources, vocal tract systems and prosodic features in order to enhance the emotion recognition "]
[{"$nulls":4},"performance;  and proposed multi-stage and hybrid models for improving the emotion recognition performance."]