<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this paper we study the cross-language speech emotion recognition using high-order Markov random fields, especially the application in Vietnamese speech emotion recognition. First, we extract the basic speech features including pitch frequency, formant frequency and short-term intensity. Based on the low level descriptor we further construct the statistic features including maximum, minimum, mean and standard deviation. Second, we adopt the high-order Markov random fields (MRF) to optimize the cross-language speech emotion model. The dimensional restrictions may be modeled by MRF. Third, based on the Vietnamese and Chinese database we analyze the efficiency of our emotion recognition system. We adopt the dimensional emotion model (arousal-valence) to verify the efficiency of MRF configuration method. The experimental results show that the high-order Markov random fields can improve the dimensional emotion recognition in the cross-language experiments, and the configuration method shows promising robustness over different languages.</p></abstract>

In this paper we study the cross-language speech emotion recognition using high-order Markov random fields, especially the application in Vietnamese speech emotion recognition. First, we extract the basic speech features including pitch frequency, formant frequency and short-term intensity. Based on the low level descriptor we further construct the statistic features including maximum, minimum, mean and standard deviation. Second, we adopt the high-order Markov random fields (MRF) to optimize the cross-language speech emotion model. The dimensional restrictions may be modeled by MRF. Third, based on the Vietnamese and Chinese database we analyze the efficiency of our emotion recognition system. We adopt the dimensional emotion model (arousal-valence) to verify the efficiency of MRF configuration method. The experimental results show that the high-order Markov random fields can improve the dimensional emotion recognition in the cross-language experiments, and the configuration method shows promising robustness over different languages.

High-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition

School of Electronics and Information Engineering, Jinling Institute of Technology, Nanjing, China

College of Physics, Optoelectronics and Energy, Soochow University, Suzhou, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In this paper we study the cross-language speech emotion recognition using high-order Markov random fields, especially the application in Vietnamese speech...

High-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition

Publié en ligne: 26 nov. 2015

Pages: 50 - 57

DOI: https://doi.org/10.1515/cait-2015-0054

Mots clés
High-order Markov random fields, speech emotion recognition, crossdatabase recognition, dimensional emotion model

© by Jiang Zhipeng

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

High-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition

Publié en ligne: 26 nov. 2015

Pages: 50 - 57

DOI: https://doi.org/10.1515/cait-2015-0054

Mots clésHigh-order Markov random fields, speech emotion recognition, crossdatabase recognition, dimensional emotion model

© by Jiang Zhipeng

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
High-order Markov random fields, speech emotion recognition, crossdatabase recognition, dimensional emotion model