Training universal background models with restricted data for speech emotion recognition

[1]  Paul Ekman,et al.  What Scientists Who Study Emotion Agree About , 2016, Perspectives on psychological science : a journal of the Association for Psychological Science.

[2]  Kris Demuynck,et al.  Cross-lingual Speech Emotion Recognition through Factor Analysis , 2018, INTERSPEECH.

[3]  Aurobinda Routray,et al.  Databases, features and classifiers for speech emotion recognition: a review , 2018, International Journal of Speech Technology.

[4]  Martin Hofmann,et al.  Support Vector Machines — Kernels and the Kernel Trick , 2006 .

[5]  Ioannis Arapakis,et al.  Theories, methods and current research on emotions in library and information science, information retrieval and human-computer interaction , 2011, Inf. Process. Manag..

[6]  Efthymios Tzinis,et al.  Integrating Recurrence Dynamics for Speech Emotion Recognition , 2018, INTERSPEECH.

[7]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[8]  Dacher Keltner,et al.  Understanding multimodal emotional expressions: recent advances in basic emotino theory , 2017 .

[9]  Klaus R. Scherer,et al.  The Role of Perceived Voice and Speech Characteristics in Vocal Emotion Communication , 2014 .

[10]  Rajib Rana,et al.  Transfer Learning for Improving Speech Emotion Classification Accuracy , 2018, INTERSPEECH.

[11]  Haimo Zhang,et al.  Speech Emotion Recognition 'in the Wild' Using an Autoencoder , 2020, INTERSPEECH.

[12]  Fabio Valente,et al.  The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism , 2013, INTERSPEECH.

[13]  H. Ralambondrainy,et al.  A conceptual version of the K-means algorithm , 1995, Pattern Recognit. Lett..

[14]  Yongming Huang,et al.  Feature fusion methods research based on deep belief networks for speech emotion recognition under noise condition , 2017, Journal of Ambient Intelligence and Humanized Computing.

[15]  Haizhou Li,et al.  Relevance factor of maximum a posteriori adaptation for GMM-NAP-SVM in speaker and language recognition , 2015, Comput. Speech Lang..

[16]  Dimitrios Tzovaras,et al.  Acoustic Scene Classification: From a Hybrid Classifier to Deep Learning , 2017, DCASE.

[17]  Elmar Nöth,et al.  A Survey on perceived speaker traits: Personality, likability, pathology, and the first challenge , 2015, Comput. Speech Lang..

[18]  Li Lee,et al.  Speaker normalization using efficient frequency warping procedures , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[19]  Shashidhar G. Koolagudi,et al.  Emotion recognition from speech: a review , 2012, International Journal of Speech Technology.

[20]  Uma Shanker Tiwary,et al.  Affect representation and recognition in 3D continuous valence–arousal–dominance space , 2016, Multimedia Tools and Applications.

[21]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[22]  WollmerMartin,et al.  Cross-Corpus Acoustic Emotion Recognition , 2010 .

[23]  Yuan Liu,et al.  EEG-based emotion recognition using an improved radial basis function neural network , 2020 .

[24]  Fabien Ringeval,et al.  Affective and behavioural computing: Lessons learnt from the First Computational Paralinguistics Challenge , 2019, Comput. Speech Lang..

[25]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[26]  K. Scherer,et al.  Introducing the Geneva Multimodal expression corpus for experimental research on emotion perception. , 2012, Emotion.

[27]  Wonyong Sung,et al.  A statistical model-based voice activity detection , 1999, IEEE Signal Processing Letters.

[28]  Louis C. W. Pols,et al.  Spectral analysis and identification of Dutch vowels in monosyllabic words , 1977 .

[29]  Björn W. Schuller,et al.  The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats , 2018, INTERSPEECH.

[30]  Dimitrios Tzovaras,et al.  Audio content analysis for unobtrusive event detection in smart homes , 2020, Eng. Appl. Artif. Intell..

[31]  John H. L. Hansen,et al.  A Study on Universal Background Model Training in Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[32]  Murray Alpert,et al.  Emotion in Speech: The Acoustic Attributes of Fear, Anger, Sadness, and Joy , 1999, Journal of psycholinguistic research.

[33]  Yi-Ping Phoebe Chen,et al.  Acoustic feature selection for automatic emotion recognition from speech , 2009, Inf. Process. Manag..

[34]  Feng Chen,et al.  ICA-Based EEG Feature Analysis and Classification of Learning Styles , 2019, 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech).

[35]  Min-Chai Hsieh,et al.  An emotion recognition mechanism based on the combination of mutual information and semantic clues , 2012, J. Ambient Intell. Humaniz. Comput..

[36]  Bayya Yegnanarayana,et al.  Analysis of Emotional Speech - A Review , 2016, Toward Robotic Socially Believable Behaving Systems.

[37]  Antonio Fernández-Caballero,et al.  Facial expression recognition in ageing adults: from lab to ambient assisted living , 2017, J. Ambient Intell. Humaniz. Comput..

[38]  Adnan Yazici,et al.  Speech emotion recognition with deep convolutional neural networks , 2020, Biomed. Signal Process. Control..

[39]  Björn W. Schuller,et al.  The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing , 2016, IEEE Transactions on Affective Computing.

[40]  K. Sreenivasa Rao,et al.  Modification of energy spectra, epoch parameters and prosody for emotion conversion in speech , 2017, Int. J. Speech Technol..

[41]  Mark H. Davis.  Empathy: A Social Psychological Approach , 1994 .

[42]  Remco C. Veltkamp,et al.  Studying emotion induced by music through a crowdsourcing game , 2016, Inf. Process. Manag..

[43]  Philip A. Kragel,et al.  Decoding the Nature of Emotion in the Brain , 2016, Trends in Cognitive Sciences.

[44]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[45]  J. Suykens,et al.  Indefinite kernels in least squares support vector machines and principal component analysis , 2017 .