Kernel multimodal discriminant analysis for speaker verification

• Published in: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing pp. 4498 - 4501
• Main Authors: Min-Seok Kim, Il-Ho Yang, Ha-Jin Yu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2010
• Subjects: Computational complexity; Covariance matrix; Feature extraction; Filtering; Kernel; Large-scale systems; Principal component analysis; Scattering; Speaker recognition; Speech enhancement; Working environment noise

In this paper, we propose a robust speaker feature extraction method using kernel multimodal Fisher discriminant analysis (kernel MFDA). Kernel MFDA has been designed to have the characteristics both of kernel principal component analysis (kernel PCA) and kernel Fisher discriminant analysis (kernel...