Deep scattering network for speech emotion recognition - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Deep scattering network for speech emotion recognition

Premjeet Singh
  • Function : Author
  • PersonId : 1090407
Goutam Saha
  • Function : Author
  • PersonId : 1090408


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 melfrequency cepstral coefficients (MFCCs) over different databases. It is observed that frequency scattering performs better than time-domain scattering and MFCCs. We also investigate layerwise 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.
Fichier principal
Vignette du fichier
Scatnet_eusipco.pdf (448.99 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03218278 , version 1 (05-05-2021)



Premjeet Singh, Goutam Saha, Md Sahidullah. Deep scattering network for speech emotion recognition. EUSIPCO 2021 - 29th European Signal Processing Conference, Aug 2021, Dublin / Virtual, Ireland. ⟨10.23919/EUSIPCO54536.2021.9615958⟩. ⟨hal-03218278⟩
109 View
166 Download



Gmail Mastodon Facebook X LinkedIn More