Speech emotion recognition by using complex MFCC and deep sequential model

Speech Emotion Recognition (SER) is one of the front-line research areas. For a machine, inferring SER is difficult because emotions are subjective and annotation is challenging. Nevertheless, researchers feel that SER is possible because speech is quasi-stationery and emotions are declarative finit...

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Veröffentlicht in:	Multimedia tools and applications Jg. 82; H. 8; S. 11897 - 11922
1. Verfasser:	Patnaik, Suprava
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.03.2023 Springer Nature B.V
Schlagworte:	Accuracy Acknowledgment Annotations Circumplex models Classification Classifiers Computer Communication Networks Computer Science Confusion Data Structures and Information Theory Datasets Emotion recognition Emotions Linguistics Matrices Multimedia Information Systems Phonemes Phonetics Prosody Recognition Researcher subject relations Robustness Special Purpose and Application-Based Systems Speech Speech recognition Speeches Subjectivity Speech emotion 1-D CNN Emotion circumplex MFCC
ISSN:	1380-7501, 1573-7721
Online-Zugang:	Volltext
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