Izvorni znanstveni članak
https://doi.org/10.7305/automatika.2016.07.853
Locally Discriminant Diffusion Projection and Its Application in Speech Emotion Recognition
Xinzhou Xu
; Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing, China
Chengwei Huang
; School of Physical Science and Technology, Soochow University, Suzhou, China
Chen Wu
; Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing, China
Li Zhao
; Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Key Laboratory of Child Development and Learning Science of Ministry of Education, Soochow University, Nanjing, China
Sažetak
The existing Diffusion Maps method brings diffusion to data samples by Markov random walk. In this paper, to provide a general solution form of Diffusion Maps, first, we propose the generalized single-graph-diffusion embedding framework on the basis of graph embedding framework. Second, by designing the embedding graph of the framework, an algorithm, namely Locally Discriminant Diffusion Projection (LDDP), is proposed for speech emotion recognition. This algorithm is the projection form of the improved Diffusion Maps, which includes both discriminant information and local information. The linear or kernelized form of LDDP (i.e., LLDDP or KLDDP) is used to achieve the dimensionality reduction of original speech emotion features. We validate the proposed algorithm on two widely used speech emotion databases, EMO-DB and eNTERFACE'05. The experimental results show that the proposed LDDP methods, including LLDDP and KLDDP, outperform some other state-of-the-art dimensionality reduction methods which are based on graph embedding or discriminant analysis.
Ključne riječi
diffusion maps; graph embedding framework; locally discriminant diffusion projection; speech emotion recognition
Hrčak ID:
165487
URI
Datum izdavanja:
1.9.2016.
Posjeta: 1.173 *