SPEECH EMOTION RECOGNITION USING SUPPORT VECTOR MACHINE

By Yixiong Pan, Peipei Shen, Liping Shen. 2010

435 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/35ebb8a93bd2ccd56a06d0b96a5e89214e89399f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Y. Gorodnichenko
2. Oleksandr Talavera
3. Antonio Liotta
4. Weifeng Ge
5. Tho Pham

In Experimental and Cognitive Psychology

1. Syed Asif Ahmad Qadri
2. Y. Gorodnichenko
3. Oleksandr Talavera
4. Taiba Maijd Wani
5. Teddy Surya Gunawan
6. Mira Kartiwi
7. Tho Pham
8. Eliathamby Ambikairajah

In Signal Processing

1. Syed Asif Ahmad Qadri
2. Y. Gorodnichenko
3. Oleksandr Talavera
4. Taiba Maijd Wani
5. Teddy Surya Gunawan
6. Antonio Liotta
7. Mira Kartiwi
8. Weifeng Ge

Citations in the corpus

• A Comprehensive Review of Speech Emotion Recognition Systems (2021, methodology): “SVM has been used as a classifier in [54] and was trained over Berlin Emo-DB and Chinese emotional databases (self-built).”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “The commonly used ML-based classifiers are SVM [183,253], HMM [179], GMM [180], RF [306], KNN [337] and ANN [374], of which the SVM classifier is the most effective one, and is indeed used in most tasks of ML-based affective analysis.”
• The Voice of Monetary Policy (2021, positive): “For example, employing the high-frequency event study approach, Rosa (2011b) shows that surprise hawkish FOMC statements lead to a reduction in equity returns.”