화자식별을 위한 전역 공분산에 기반한 주성분분석
- Authors
- 서창우; 임영환
- Issue Date
- 2009
- Publisher
- 한국음성학회
- Keywords
- speaker identification; principal component analysis; global covariance; Gaussian mixture model; eigenvalue; eigenvactor; speaker identification; principal component analysis; global covariance; Gaussian mixture model; eigenvalue; eigenvactor
- Citation
- 말소리와 음성과학, v.1, no.1, pp.69 - 73
- Journal Title
- 말소리와 음성과학
- Volume
- 1
- Number
- 1
- Start Page
- 69
- End Page
- 73
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/16424
- ISSN
- 2005-8063
- Abstract
- This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Information Technology > Global School of Media > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/16424)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.