• DOI:10.1109/ICCCI50826.2021.9402569
• Corpus ID: 231846518

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}
}

• Premjeet Singh, G. Saha, M. Sahidullah
• Published 27 January 2021
• Computer Science
• 2021 International Conference on Computer Communication and Informatics (ICCCI)

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 comparative analysis of short-term acoustic features based on STFT and CQT for SER with deep neural network (DNN) as a back-end classifier. We optimize different parameters for both features. The CQT-based features outperform the STFT-based spectral features for SER experiments. Further experiments with cross-corpora evaluation demonstrate that the CQT-based systems provide better generalization with out-of-domain training data.