Language Independent Emotion Quantification using Non linear Modelling of Speech

TL;DR

This paper presents a novel approach to emotion quantification from speech using nonlinear multifractal analysis, capturing speech complexity and distinguishing emotions effectively across different speaking styles.

Contribution

It introduces nonlinear multifractal modeling techniques for emotion detection in speech, considering speaker-specific vocal features and demonstrating improved emotion clustering.

Findings

Multifractal spectral width differentiates emotions in speech.

Nonlinear models effectively cluster emotional speech signals.

Speech complexity correlates with emotional states.

Abstract

At present emotion extraction from speech is a very important issue due to its diverse applications. Hence, it becomes absolutely necessary to obtain models that take into consideration the speaking styles of a person, vocal tract information, timbral qualities and other congenital information regarding his voice. Our speech production system is a nonlinear system like most other real world systems. Hence the need arises for modelling our speech information using nonlinear techniques. In this work we have modelled our articulation system using nonlinear multifractal analysis. The multifractal spectral width and scaling exponents reveals essentially the complexity associated with the speech signals taken. The multifractal spectrums are well distinguishable the in low fluctuation region in case of different emotions. The source characteristics have been quantified with the help of different non-linear models like Multi-Fractal Detrended Fluctuation Analysis, Wavelet Transform Modulus Maxima. The Results obtained from this study gives a very good result in emotion clustering.