title: Minimum classification error-based weighted support vector machine kernels for speaker verification.
abstract: Support vector machines (SVMs) have been proved to be an effective approach to speaker verification. An appropriate selection of the kernel function is a key issue in SVM-based classification. In this letter, a new SVM-based speaker verification method utilizing weighted kernels in the Gaussian mixture model supervector space is proposed. The weighted kernels are derived by using the discriminative training approach, which minimizes speaker verification errors. Experiments performed on the NIST 2008 speaker recognition evaluation task showed that the proposed approach provides substantially improved performance over the baseline kernel-based method.
authors.affiliation: Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, South Korea. yjsuh@kaist.ac.kr
authors.forename: Youngjoo
authors.initials: Y
authors.lastname: Suh
authors.forename: Hoirin
authors.initials: H
authors.lastname: Kim
mesh_terms: D000162:Acoustics; D056667:Biometric Identification; D016002:Discriminant Analysis; D006801:Humans; D008962:Models, Theoretical; D012815:Signal Processing, Computer-Assisted; D013061:Speech Acoustics; D049250:Speech Recognition Software; D060388:Support Vector Machine