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Neural Networks and TEO Features for an Automatic Recognition of Stress in Spontaneous Speech.

, , , and . ICNC (2), page 227-231. IEEE Computer Society, (2009)

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Optimization of a temporal decomposition model of speech., , and . ISSPA, page 471-474. IEEE, (1999)Modeling subjectiveness in emotion recognition with deep neural networks: Ensembles vs soft labels., , and . IJCNN, page 566-570. IEEE, (2016)Statistical differences in speech acoustics of major depressed and non-depressed adolescents., , and . ICSPCS, page 1-7. IEEE, (2015)Speech Acoustic Features Characterising Individuals with High and Low Public Trust., , , and . ICSPCS, page 1-9. IEEE, (2019)LoRa Signal Demodulation Using Deep Learning, a Time-Domain Approach., , , and . VTC Spring, page 1-6. IEEE, (2021)A Comparison between Anatomy-Based and Data-Driven Tree Models for Human Pose Estimation., , , and . DICTA, page 1-7. IEEE, (2017)Sleep Stage Recognition from EEG Using a Distributed Multi-Channel Decision-Making System., , and . ICSPCS, page 1-7. IEEE, (2021)Recognition of stress in speech using wavelet analysis and Teager energy operator., , , and . INTERSPEECH, page 605-608. ISCA, (2008)Speaker Verification Based on Different Vector Quantization Techniques with Gaussian Mixture Models., , and . NSS, page 403-408. IEEE Computer Society, (2009)Real time speech emotion recognition using RGB image classification and transfer learning., , , and . ICSPCS, page 1-8. IEEE, (2017)