Non-linear frequency warping using constant-Q transformation for speech emotion recognition

@article{Singh2021NonlinearFW,
  title={Non-linear frequency warping using constant-Q transformation for speech emotion recognition},
  author={Premjeet Singh and Goutam Saha and Md. Sahidullah},
  journal={2021 International Conference on Computer Communication and Informatics (ICCCI)},
  year={2021},
  pages={1-6}
}
In this work, we explore the constant-Q transform (CQT) for speech emotion recognition (SER). The CQT-based time-frequency analysis provides variable spectro-temporal resolution with higher frequency resolution at lower frequencies. Since lower-frequency regions of speech signal contain more emotion-related information than higher-frequency regions, the increased low-frequency resolution of CQT makes it more promising for SER than standard short-time Fourier transform (STFT). We present a… Expand
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Deep scattering network for speech emotion recognition
TLDR
This paper investigates layerwise scattering coefficients to analyse the importance of time shift and deformation stable scalogram and modulation spectrum coefficients for SER and observes that layer-wise coefficients taken independently also perform better than MFCCs. Expand

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