Emotion Recognition from Speech: A Survey

Georgios Drakopoulos, George Pikramenos, Evaggelos Spyrou, Stavros Perantonis

2019

Abstract

Emotion recognition from speech signals is an important field in its own right as well as a mainstay of many multimodal sentiment analysis systems. The latter may as well include a broad spectrum of modalities which are strongly associated with consciously or subconsciously communicating human emotional state such as visual cues, gestures, body postures, gait, or facial expressions. Typically, emotion discovery from speech signals not only requires considerably less computational complexity than other modalities, but also at the same time in the overwhelming majority of studies the inclusion of speech modality increases the accuracy of the overall emotion estimation process. The principal algorithmic cornerstones of emotion estimation from speech signals are Hidden Markov Models, time series modeling, cepstrum processing, and deep learning methodologies, the latter two being prime examples of higher order data processing. Additionally, the most known datasets which serve as emotion recognition benchmarks are described.

Paper Citation

in Harvard Style

Drakopoulos G., Pikramenos G., Spyrou E. and Perantonis S. (2019). Emotion Recognition from Speech: A Survey.In Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-386-5, pages 432-439. DOI: 10.5220/0008495004320439

in Bibtex Style

@conference{webist19,
author={Georgios Drakopoulos and George Pikramenos and Evaggelos Spyrou and Stavros Perantonis},
title={Emotion Recognition from Speech: A Survey},
booktitle={Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2019},
pages={432-439},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008495004320439},
isbn={978-989-758-386-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Emotion Recognition from Speech: A Survey
SN - 978-989-758-386-5
AU - Drakopoulos G.
AU - Pikramenos G.
AU - Spyrou E.
AU - Perantonis S.
PY - 2019
SP - 432
EP - 439
DO - 10.5220/0008495004320439