article

Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011

Authors:

Christos-Nikolaos Anagnostopoulos,

Theodoros Iliou,

Ioannis GiannoukosAuthors Info & Claims

Artificial Intelligence Review, Volume 43, Issue 2

Pages 155 - 177

https://doi.org/10.1007/s10462-012-9368-5

Published: 01 February 2015 Publication History

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.

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Cited By

Luo S(2024)Navigating the Diverse Challenges of Speech Emotion Recognition: A Deep Learning PerspectiveProceedings of the 27th International Academic Mindtrek Conference10.1145/3681716.3681725(133-146)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3681716.3681725
George SMuhamed Ilyas P(2024)A review on speech emotion recognitionNeurocomputing10.1016/j.neucom.2023.127015568:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127015
Li ZZhang GOkada SWang LZhao BDang J(2024)MBCFNetKnowledge-Based Systems10.1016/j.knosys.2024.111826296:COnline publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111826
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Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 43, Issue 2

February 2015

155 pages

ISSN:0269-2821

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Copyright © Copyright © 2015 Springer Science+Business Media Dordrecht.

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Luo S(2024)Navigating the Diverse Challenges of Speech Emotion Recognition: A Deep Learning PerspectiveProceedings of the 27th International Academic Mindtrek Conference10.1145/3681716.3681725(133-146)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3681716.3681725
George SMuhamed Ilyas P(2024)A review on speech emotion recognitionNeurocomputing10.1016/j.neucom.2023.127015568:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127015
Li ZZhang GOkada SWang LZhao BDang J(2024)MBCFNetKnowledge-Based Systems10.1016/j.knosys.2024.111826296:COnline publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111826
Raheel A(2024)Emotion analysis and recognition in 3D space using classifier-dependent feature selection in response to tactile enhanced audio–visual content using EEGComputers in Biology and Medicine10.1016/j.compbiomed.2024.108807179:COnline publication date: 18-Oct-2024
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Bhangale KKothandaraman M(2024)Speech Emotion Recognition Using Generative Adversarial Network and Deep Convolutional Neural NetworkCircuits, Systems, and Signal Processing10.1007/s00034-023-02562-543:4(2341-2384)Online publication date: 1-Apr-2024
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Gu YZhang XYan HHuang JLiu ZDong MRen F(2023)WiFE: WiFi and Vision Based Unobtrusive Emotion Recognition via Gesture and Facial ExpressionIEEE Transactions on Affective Computing10.1109/TAFFC.2023.328577714:4(2567-2581)Online publication date: 1-Oct-2023
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Gerczuk MAmiriparian SOttl SSchuller B(2023)EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion RecognitionIEEE Transactions on Affective Computing10.1109/TAFFC.2021.313515214:2(1472-1487)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TAFFC.2021.3135152
Hashem AArif MAlghamdi M(2023)Speech emotion recognition approachesSpeech Communication10.1016/j.specom.2023.102974154:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.specom.2023.102974
Palo HSubudhiray SDas N(2023)The amalgamation of wavelet packet information gain entropy tuned source and system parameters for improved speech emotion recognitionSpeech Communication10.1016/j.specom.2023.03.007149:C(11-28)Online publication date: 1-Apr-2023
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Kaur KSingh P(2023)Trends in speech emotion recognition: a comprehensive surveyMultimedia Tools and Applications10.1007/s11042-023-14656-y82:19(29307-29351)Online publication date: 22-Feb-2023
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