Results 1 to 10 of about 15,605 (256)

FedEmoNet: Privacy-preserving federated learning with TCN-Transformer fusion for cross-corpus speech emotion recognition. [PDF]

PLoS One
Tawfik M, Obeidat RA, Kamel S, Aljarrah NA, Shehadeh HH, Dalalah A.   +5 more
europepmc   +1 more source

Speech Emotion Recognition Based on Modified ReliefF. [PDF]

Sensors (Basel), 2022
Li GM, Liu N, Zhang JA.
europepmc   +1 more source

Deep cross-modal affective memory networks with adaptive multi-source heterogeneous transfer learning in speech emotion recognition. [PDF]

Sci Rep
Zhao X, Liu J, Lin L.
europepmc   +1 more source

Stressed Speech Emotion Recognition Using Teager Energy and Spectral Feature Fusion with Feature Optimization. [PDF]

Comput Intell Neurosci, 2023
Bandela SR, Siva Priyanka S, Sunil Kumar K, Vijay Bhaskar Reddy Y, Berhanu AA.   +4 more
europepmc   +1 more source

Assessing random forest performance in low resource speech emotion recognition. [PDF]

Sci Rep
Adeel M, Tao ZY, Jin SY, Guo CJ, Alsuhaibani M.   +4 more
europepmc   +1 more source

Representation learning with parameterised quantum circuits for advancing speech emotion recognition. [PDF]

Sci Rep
Rajapakshe T, Rana R, Riaz F, Khalifa S, Schuller BW.   +4 more
europepmc   +1 more source

IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients. [PDF]

Sensors (Basel), 2023
Olatinwo DD, Abu-Mahfouz A, Hancke G, Myburgh H.   +3 more
europepmc   +1 more source

Speech analysis and speech emotion recognition in mental disease: a scoping review. [PDF]

Front Psychol
Lombardo C, Esposito G, Carbone S, Serrano S, Mento C.   +4 more
europepmc   +1 more source

Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest. [PDF]

PLoS One, 2023
Rezapour Mashhadi MM, Osei-Bonsu K.
europepmc   +1 more source

Optimization of speech emotion recognition using hybrid dataset integration and deep learning-based feature fusion with a novel balanced focal entropy loss. [PDF]

Sci Rep
Nagro S.
europepmc   +1 more source