  This paper introduces scattering transform for speech emotion recognition
(SER). Scattering transform generates feature representations which remain
stable to deformations and shifting in time and frequency without much loss of
information. In speech, the emotion cues are spread across time and localised
in frequency. The time and frequency invariance characteristic of scattering
coefficients provides a representation robust against emotion irrelevant
variations e.g., different speakers, language, gender etc. while preserving the
variations caused by emotion cues. Hence, such a representation captures the
emotion information more efficiently from speech. We perform experiments to
compare scattering coefficients with standard mel-frequency cepstral
coefficients (MFCCs) over different databases. It is observed that frequency
scattering performs better than time-domain scattering and MFCCs. We also
investigate layer-wise scattering coefficients to analyse the importance of
time shift and deformation stable scalogram and modulation spectrum
coefficients for SER. We observe that layer-wise coefficients taken
independently also perform better than MFCCs.
