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Communication Dans Un Congrès Année : 2021

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

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

Résumé

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.
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Dates et versions

hal-03134015 , version 1 (08-02-2021)

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Premjeet Singh, Goutam Saha, Md Sahidullah. Non-linear frequency warping using constant-Q transformation for speech emotion recognition. ICCCI 2021 - International Conference on Computer Communication and Informatics, Jan 2021, Coimbatore, India. ⟨10.1109/ICCCI50826.2021.9402569⟩. ⟨hal-03134015⟩
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