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Emotional Analysis Using Multinomial Logistic Regression

Affiliations

  • Computer Science and Engineering Department, SRM University, Chennai - 603203, Tamil Nadu, India

Abstract


Background: Emotions have been widely used in psychology and behavior sciences as they are important elements of human nature. We expect a machine to behave like a human, in this digital era. Since the machines do not understand the emotional state of the speaker easily, it is not very easy to get a natural interaction between machine and man. Yet, many researchers are working and progressing in speech recognition. In this paper we aim to identify emotions present in human beings through analysis of speech signals. Methods: We make use of machine learning algorithm to choose the best features which effectively influence the emotional states of speech. Findings: We aim to identify various emotions that a human being goes through during verbal communication. Applications: Computation on the chosen best features of speech signals help us identifying the speaker’s emotional state.

Keywords

Emotions, Logistic Regression, Machine Learning, SAVEE, Speech, Hypothesis.

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References


  • Fernandez R. A computational model for the automatic recognition of affect in speech [Doctoral dissertation]. Massachusetts Institute of Technology; 2004 Feb.
  • Cahn JE. The generation of a ECT in synthesized speech. Journal of the American Voice I/O Society. 1990 Jul; 8:1–9.
  • El Ayadi M, Kamel MS, Karray F. Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition. 2011 Mar 31; 44(3):572–87.
  • Sharma K, Singh P. Speech recognition of Punjabi numerals using synergic HMM and DTW approach. Indian Journal of Science and Technology. 2015 Oct 16; 8(27).
  • Ratanpal BS, Sahni S. On speech synthesis of Sindhi numeric. Indian Journal of Science and Technology. 2015 Oct 18; 8(27).
  • Koolagudi SG, Maity S, Kumar VA, Chakrabarti S, Rao KS. IITKGP-SESC: Speech database for emotion analysis. International Conference on Contemporary Computing; Springer Berlin Heidelberg. 2009 Aug 17. p. 485–92.
  • Nwe TL, Foo SW, De Silva LC. Speech emotion recognition using hidden Markov models. Speech Communication. 2003 Nov 30; 41(4):603–23.
  • Surrey Audio-Visual Expressed Emotion (SAVEE) Database. Available from: http://personal.ee.surrey.ac.uk/Personal/P.Jackson/SAVEE/ 9. Praat: Doing phonetics by computer. Available from: http://www.fon.hum.uva.nl/praat/

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