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Analysis of Speech Emotions Using Dynamics of Prosodic Parameters

  • Hemanta Kumar Palo
  • Mihir N. MohantyEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)

Abstract

In this paper, an attempt is made to explore the dynamics of speech prosody to characterize and classify emotional states in a speech signal. The local or fine variations describing the prosodic dynamics are combined with the static prosodic parameters for a possible enhancement in the emotional speech recognition (ESR) accuracy. The efficient vector quantization (VQ) clustering algorithm has been applied to compress the static and dynamic parameters before further processing in a radial basis neural network (RBFNN) platform. Results reveal an improvement in ESR accuracy of 86.05% by involving both static and dynamic prosodic features as compared to 84.92% accuracy when the combination of static prosodic feature simulated alone.

Keywords

Speech emotion Speech prosody Feature extraction Radial basis function neural network Classification 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringITER, Siksha ‘O’ Anusandhan UniversityBhubaneswarIndia

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