%0 Journal Article %1 AnagnostopoulosIliouGiannoukos15air %A Anagnostopoulos, Christos-Nikolaos %A Iliou, Theodoros %A Giannoukos, Ioannis %D 2015 %J Artificial Intelligence Review %K v1500 springer paper ai language processing speech emotion recognition zzz.mmi %N 2 %P 155-177 %R 10.1007/s10462-012-9368-5 %T Features and Classifiers for Emotion Recognition from Speech: A Survey from 2000 to 2011 %V 43 %X Speaker emotion recognition is achieved through processing methods that include isolation of the speech signal and extraction of selected features for the final classification. In terms of acoustics, speech processing techniques offer extremely valuable paralinguistic information derived mainly from prosodic and spectral features. In some cases, the process is assisted by speech recognition systems, which contribute to the classification using linguistic information. Both frameworks deal with a very challenging problem, as emotional states do not have clear-cut boundaries and often differ from person to person. In this article, research papers that investigate emotion recognition from audio channels are surveyed and classified, based mostly on extracted and selected features and their classification methodology. Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade. This survey also provides a discussion on open trends, along with directions for future research on this topic.

@article{AnagnostopoulosIliouGiannoukos15air, abstract = {Speaker emotion recognition is achieved through processing methods that include isolation of the speech signal and extraction of selected features for the final classification. In terms of acoustics, speech processing techniques offer extremely valuable paralinguistic information derived mainly from prosodic and spectral features. In some cases, the process is assisted by speech recognition systems, which contribute to the classification using linguistic information. Both frameworks deal with a very challenging problem, as emotional states do not have clear-cut boundaries and often differ from person to person. In this article, research papers that investigate emotion recognition from audio channels are surveyed and classified, based mostly on extracted and selected features and their classification methodology. Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade. This survey also provides a discussion on open trends, along with directions for future research on this topic.}, added-at = {2015-02-22T11:36:31.000+0100}, author = {Anagnostopoulos, Christos-Nikolaos and Iliou, Theodoros and Giannoukos, Ioannis}, biburl = {https://www.bibsonomy.org/bibtex/2903e73ce4935c3d08883022605ea6efe/flint63}, doi = {10.1007/s10462-012-9368-5}, file = {SpringerLink:2015/AnagnostopoulosIliouGiannoukos15air.pdf:PDF}, groups = {public}, interhash = {81dce25debb7e158e749e983a9287690}, intrahash = {903e73ce4935c3d08883022605ea6efe}, issn = {0269-2821}, journal = {Artificial Intelligence Review}, keywords = {v1500 springer paper ai language processing speech emotion recognition zzz.mmi}, month = {#feb#}, number = 2, pages = {155-177}, timestamp = {2018-04-16T11:46:22.000+0200}, title = {Features and Classifiers for Emotion Recognition from Speech: A Survey from 2000 to 2011}, username = {flint63}, volume = 43, year = 2015 }