Speech Emotion Recognition using Support Vector Machine

Abhijit Bhowmick; Bharath K P; Karthik R; Manas Jain; Pratibha Balaji; Rajesh Kumar Muthu; Shruthi Narayan

Speech Emotion Recognition using Support Vector Machine

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arxiv 2002.07590 v1 pith:VXLW66EA submitted 2020-02-03 eess.AS cs.IRcs.SD

Manas Jain , Shruthi Narayan , Pratibha Balaji , Bharath K P , Abhijit Bhowmick , Karthik R , Rajesh Kumar Muthu This is my paper · ORCID

classification eess.AScs.IRcs.SD

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In this project, we aim to classify the speech taken as one of the four emotions namely, sadness, anger, fear and happiness. The samples that have been taken to complete this project are taken from Linguistic Data Consortium (LDC) and UGA database. The important characteristics determined from the samples are energy, pitch, MFCC coefficients, LPCC coefficients and speaker rate. The classifier used to classify these emotional states is Support Vector Machine (SVM) and this is done using two classification strategies: One against All (OAA) and Gender Dependent Classification. Furthermore, a comparative analysis has been conducted between the two and LPCC and MFCC algorithms as well.

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Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition
cs.SD 2025-06 conditional novelty 5.0 of 10

Adding speaker-contrastive or BYOL self-supervised pretraining to a Whisper-based model improves low-resource speech emotion recognition on Urdu, German, and Bangla.

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