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Improving Speech Emotion Recognition System Using Spectral and Prosodic Features

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

Abstract

The detection of emotions from speech is a key aspect of all human behaviors, Speech Emotion Recognition (SER) plays an extensive role in a diverse range of applications, especially in human-computer communication. The main aim of this study is to build two Machine Learning (ML) models able to classify the input speech into several classes of emotions. In contrast, we extract a set of prosodic and spectral features from sound files and apply a feature selection method to improve the SER rate of the proposed system. Experiments are being done to evaluate the accuracy of the emotional speech system with the use of the RAVDESS database. We performed the efficiency of our models and compared them to the existing literature for SER. Our obtained results indicate that the proposed system based on Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) achieves a test accuracy of \(69.67\%\) and \(65.04\%\) respectively with 8 emotional states.

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Acknowledgements

This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco (Alkhawarizmi/2020/01).

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Authors and Affiliations

  1. Team Computer Science, Artificial Intelligence and Big Data, MCSA Laboratory, Faculty of Sciences and Technology of Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco

    Adil Chakhtouna & Abdellah Adib

  2. Higher National School of Arts and Crafts of Casablanca, Casablanca, Morocco

    Sara Sekkate

Authors
  1. Adil Chakhtouna
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  2. Sara Sekkate
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  3. Abdellah Adib
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Corresponding author

Correspondence to Adil Chakhtouna .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Chakhtouna, A., Sekkate, S., Adib, A. (2022). Improving Speech Emotion Recognition System Using Spectral and Prosodic Features. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_37

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