Related references
Note: Only part of the references are listed.Clinical state tracking in serious mental illness through computational analysis of speech
Armen C. Arevian et al.
PLOS ONE (2020)
STARGAN FOR EMOTIONAL SPEECH CONVERSION: VALIDATED BY DATA AUGMENTATION OF END-TO-END EMOTION RECOGNITION
Georgios Rizos et al.
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (2020)
X-VECTORS MEET EMOTIONS: A STUDY ON DEPENDENCIES BETWEEN EMOTION AND SPEAKER RECOGNITION
Raghavendra Pappagari et al.
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (2020)
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
Jianfeng Zhao et al.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2019)
Deep neural networks for emotion recognition combining audio and transcripts
Jaejin Cho et al.
19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES (2018)
Emotion Identification from raw speech signals using DNNs
Mousmita Sarma et al.
19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES (2018)
End-to-End Multimodal Emotion Recognition Using Deep Neural Networks
Panagiotis Tzirakis et al.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2017)
Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer's disease
K. Lopez-de-Ipina et al.
NEUROCOMPUTING (2015)
Emotion recognition from speech: a review
Shashidhar G. Koolagudi et al.
INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY (2012)
IEMOCAP: interactive emotional dyadic motion capture database
Carlos Busso et al.
LANGUAGE RESOURCES AND EVALUATION (2008)
Share Paper
