Performance Analysis of ML Algorithms on Speech Emotion Recognition

  • Pradeep TiwariEmail author
  • Anand D. Darji
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1086)


Even though human–computer interface (HCI) applications such as computer-aided tutoring, learning and medical assistance have brought much changes in human lifestyle. This work has mainly focused on comparison of performance of five commonly used classifiers on emotion recognition. Since features are usually high-dimensional and structurally complex, the efficient classification has become more challenging particularly on low-cost processor and mobile (Android) environment. In this work, five machine-learning algorithms are implemented for speaker-independent emotion recognition and their performance is compared: (a) Logistic regression (LR), (b) K-nearest neighbour (KNN), (c) Naive Bayesian classifier (B), (d) Support vector machine (SVM), and (e) Multilayer perceptron (MLP) of neural network. The feature extraction techniques used to obtain features from speech are (a) Mel-scaled power spectrum; (b) Mel frequency cepstral coefficients. Naive Bayes classifier shows best results in speech emotion classification among other classifiers. Emotion data of happy and sad is taken from Surrey Audio-Visual Expressed Emotion (SAVEE) database.


Emotion recognition MFCC Logistic regression K-nearest neighbour Support vector machine 


  1. 1.
    A. Chavhan, S. Dahe, S. Chibhade, A neural network approach for real time emotion recognition. IJARCCE 4(3), 259–263 (2015)CrossRefGoogle Scholar
  2. 2.
    C. Cameron, K. Lindquist, K. Gray, A constructionist review of morality and emotions: no evidence for specific links between moral content and discrete emotions. Pers. Soc. Psychol. Rev. 19(4), 371–394 (2015)CrossRefGoogle Scholar
  3. 3.
    J. Fredes, J. Novoa, S. King, R.M. Stern, N.B. Yoma, Locally normalized filter banks applied to deep neural-network-based robust speech recognition. IEEE Signal Process. Lett. 24(4), 377–381 (2017)CrossRefGoogle Scholar
  4. 4.
    S. Ramamohan, S. Dandpat, Sinusoidal model based analysis and classification of stressed speech. IEEE Trans. Speech Audio Process. 14(3), 737–746 (2006)Google Scholar
  5. 5.
    S. Shukla, S.R.M. Prasanna, S. Dandapat, Stressed speech processing: human vs automatic in non-professional speaker scenario, in National Conference on Communications (2011), pp. 1–5Google Scholar
  6. 6.
    Y.D. Chavhan, B.S. Yelure, K.N. Tayade, Speech emotion recognition using RBF kernel of LIBSVM, in 2nd International Conference on Electronics and Communication Systems (2015), pp. 1132–1135Google Scholar
  7. 7.
    V. Sucharita, S. Jyothi, V. Rao, Comparison of machine learning algorithms for classification of Penaeid prawn species, in 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (IEEE, 2016), pp. 1610–1613Google Scholar
  8. 8.
    H. Berg, K.T. Hjelmervik, D.H.S. Stender, T.S. Sastad, A comparison of different machine learning algorithms for automatic classification of sonar targets, in OCEANS MTS (IEEE Monterey, 2016), pp. 1–8Google Scholar
  9. 9.
    H. Fayek, M. Lech, L. Cavedon, Towards real-time Speech Emotion Recognition using deep neural networks, in 9th International Conference on Signal Processing and Communication Systems, Cairns, QLD (2015), pp. 1–5Google Scholar
  10. 10.
    P. Tiwari, U. Rane, A.D. Darji, Measuring the effect of music therapy on voiced speech signal, in Future Internet Technologies and Trends. ICFITT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 220. (Springer, 2017)Google Scholar
  11. 11.
    H. Atasoy, Emotion recognition from speech using Fisher’s discriminant analysis and Bayesian classifier, in Signal Processing and Communications Applications Conference (SIU) (IEEE, 2015), pp. 2513–2516Google Scholar
  12. 12.
    P. Jackson, S. Haq, Surrey Audio-visual Expressed Emotion (SAVEE) Database (University of Surrey, Guildford, UK, 2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.NMIMS UniversityMumbaiIndia
  2. 2.SVNITSuratIndia

Personalised recommendations