Emotion Mining from Speech in the Covid-19 Era: An Exploratory Study
2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1784480
ABSTRACT
Speech is the most effective form of communication because it is not limited to just the linguistic components but carries the speaker's emotions laced within the components like tone of voice and cues like cries and sighs. This paper aims at studying the research done in the past and applying it to the Covid-19 era.The pandemic is of a great magnitude, affecting every aspect of life including emotions. This time period requires research in determining the most dominant emotions in conversations, to serve as a reference for future research and as a contrast to the research done in the past. Previous papers have identified emotions like happiness, anger, fear and sadness using feature extraction algorithms like MFCC (Mel Frequency Cepstral Coefficients and numerous classification algorithms like GMM (Gaussian Mixture Model), SVM (Support Vector Machine), KNN (K-Nearest-neighbor) and HMM (Hidden Markov Model). Some research has pointed towards ASR (Automatic Speech Recognition), N-Grams and vector space modeling. This paper aims at recognizing the most suitable algorithms for determining the pandemic specific emotions in speech. © 2021 IEEE.
Emotion recognition; Feature extraction; GMM; SVM; Classification (of information); Extraction; Gaussian distribution; Hidden Markov models; Nearest neighbor search; Speech; Speech communication; Speech recognition; Support vector machines; Trellis codes; Vector spaces; Classification algorithm; Exploratory studies; Feature extraction algorithms; Features extraction; Gaussian Mixture Model; Mel frequency cepstral co-efficient; Mel-frequency cepstral coefficients; Support vectors machine; Time-periods
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021
Year:
2021
Document Type:
Article
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