  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.
