Editing State-of-the-art (section)

=== Speech Emotion Recognition ===

==== Grimm, M., Kroschel, K., & Narayanan, S. (2007, April). Support vector regression for automatic recognition of spontaneous emotions in speech. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07 (Vol. 4, pp. IV-1085). IEEE. ====

* Summary: The paper presents methods for estimating emotions expressed spontaneously in speech, using Support Vector Regression (SVR). It evaluates three emotion primitives—valence, activation, and dominance—showing SVR's superiority over Fuzzy Logic and Fuzzy k-Nearest Neighbor classifiers in accuracy and correlation with human assessments.
* RQ: How to estimate emotions under the conditions of (1) nonacted, spontaneous speech and (2) non-categorical, quasicontinuous emotional content.
* Hypothesis: SVR can more accurately estimate emotions in speech compared to traditional classifiers, given its ability to handle continuous emotion primitives and complex non-linear relationships in data.
* Conclusion: SVR outperforms Fuzzy Logic and k-Nearest Neighbor classifiers in estimating emotions from speech, achieving lower classification errors and higher correlations with reference emotions. This underscores SVR's suitability for continuous-valued emotion estimation in spontaneous speech.
* Critical observations: SVR yields the lowest mean classification errors and highest correlation coefficients for emotion estimation. In addition, Feature selection indicates that using 20 features suffices for accurate emotion estimation across different classifiers.
* Relevance: This study advances automatic emotion recognition in speech, crucial for improving human-machine interaction and developing emotionally intelligent systems. Future work will investigate designing a real-time system using the algorithms. The advantage of continuous-valued estimates of the emotional state of a person could be used to build an adaptive emotion tracking system that is capable to adapt to individual personalities and long-term moods.


'''Z. Huang, M. Dong, Q. Mao, and Y. Zhan, “Speech emotion recognition using cnn,” in Proceedings of the 22nd ACM international conference on Multimedia,pp. 801–804,ACM, 2014.'''

'''Summary'''：The paper introduces a CNN model that processes input data in two stages, using unlabeled samples for candidate feature extraction and then learning discriminative features under semi-supervision.

'''RQ'''：The main research question is how to efficiently and automatically extract discriminative sentiment features from speech signals for sentiment recognition, especially in complex scenarios where the speaker and environment change.

'''Hypothesis'''：The main research question is how to efficiently and automatically extract discriminative sentiment features from speech signals for sentiment recognition, especially in complex scenarios where the speaker and environment change.

'''Conclusion'''：The semi-CNN model can effectively learn emotionally skewed features to achieve consistent and robust performance in speech emotion recognition tasks.

'''Critical observations'''：Semi-CNN models benefit from a two-stage feature learning process that initially extracts candidate features without labeling the data. The use of novel objective functions to improve feature saliency, orthogonality, and discrimination helps to enhance the robustness of the model.

'''Relevance'''：It is important to facilitate human-computer interaction by improving the accuracy and reliability of speech emotion recognition systems. It contributes to the development of the field of affective computing and may influence the development of more sensitive and adaptive SER systems.