Transfer Learning for Speech Emotion Recognition

Speech emotion recognition is still challenging due to the complexity of emotion. Most of the current research is based on the enough data, and the training data and test data are from the same corpus. Obviously, this is not in line with the actual application scene. In addition, the emotion annotation of large amounts speech data is also a challenging task. This paper investigates and summarizes the concepts and categories, methods and applications of transfer learning briefly, and studies the combination of transfer learning and deep learning, and the application of speech emotion recognition, and then points out the key issues that need to be further solved in the research.

[1]  Qiang Yang,et al.  Cross-domain activity recognition , 2009, UbiComp.

[2]  Song Peng,et al.  Cross-corpus speech emotion recognition based on a feature transfer learning method , 2016 .

[3]  Björn W. Schuller,et al.  Speech emotion recognition , 2018, Commun. ACM.

[4]  Bertram E. Shi,et al.  Pose-Independent Facial Action Unit Intensity Regression Based on Multi-Task Deep Transfer Learning , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[5]  Björn W. Schuller,et al.  Multi-task deep neural network with shared hidden layers: Breaking down the wall between emotion representations , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Sebastian Ruder,et al.  An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.

[7]  Michael I. Jordan,et al.  Deep Transfer Learning with Joint Adaptation Networks , 2016, ICML.

[8]  Che-Wei Huang,et al.  Deep convolutional recurrent neural network with attention mechanism for robust speech emotion recognition , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[9]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[10]  Martial Hebert,et al.  Cross-Stitch Networks for Multi-task Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[12]  Yongzhao Zhan,et al.  Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks , 2014, IEEE Transactions on Multimedia.

[13]  Yafeng Niu,et al.  A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks , 2017, ArXiv.

[14]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[15]  Erik Marchi,et al.  Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[16]  Massimiliano Pontil,et al.  Multi-Task Feature Learning , 2006, NIPS.

[17]  Ngoc Thang Vu,et al.  Attentive Convolutional Neural Network Based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech , 2017, INTERSPEECH.

[18]  Andrew Zisserman,et al.  Emotion Recognition in Speech using Cross-Modal Transfer in the Wild , 2018, ACM Multimedia.

[19]  Ruchuan Wang,et al.  Transferring Age and Gender Attributes for Dimensional Emotion Prediction from Big Speech Data Using Hierarchical Deep Learning , 2018, 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS).

[20]  Ingo Siegert,et al.  Appropriate emotional labelling of non-acted speech using basic emotions, geneva emotion wheel and self assessment manikins , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[21]  Zhihong Zeng,et al.  A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  John Blitzer,et al.  Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.

[23]  Silvio Savarese,et al.  Cross-view action recognition via view knowledge transfer , 2011, CVPR 2011.

[24]  Scott Wisdom,et al.  Transfer Learning From Sound Representations For Anger Detection in Speech , 2019, ArXiv.

[25]  Jie Li,et al.  A Novel UDT-Based Transfer Speed-Up Protocol for Fog Computing , 2018, Wirel. Commun. Mob. Comput..

[26]  Qiang Yang,et al.  Transferring Naive Bayes Classifiers for Text Classification , 2007, AAAI.

[27]  Thierry Dutoit,et al.  ASR-based Features for Emotion Recognition: A Transfer Learning Approach , 2018, ArXiv.

[28]  Laurence Devillers,et al.  CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation , 2018, Workshop on Speech, Music and Mind (SMM 2018).

[29]  Carlos Busso,et al.  Jointly Predicting Arousal, Valence and Dominance with Multi-Task Learning , 2017, INTERSPEECH.

[30]  Fakhri Karray,et al.  Survey on speech emotion recognition: Features, classification schemes, and databases , 2011, Pattern Recognit..