About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
INTERSPEECH 2007
Conference paper
Speaker recognition using kernel-PCA and intersession variability modeling
Abstract
This paper presents a new method for text independent speaker recognition. We embed both training and test sessions into a session space. The session space is a direct sum of a common-speaker subspace and a speaker-unique subspace. The common-speaker subspace is Euclidean and is spanned by a set of reference sessions. Kernel-PCA is used to explicitly embed sessions into the common-speaker subspace. The common-speaker subspace typically captures attributes that are common to many speakers. The speaker-unique subspace is the orthogonal complement of the commonspeaker subspace and typically captures attributes that are speaker unique. We model intersession variability in the common-speaker subspace, and combine it with the information that exists in the speaker-unique subspace. Our suggested framework leads to a 43.5% reduction in error rate compared to a Gaussian Mixture Model (GMM) baseline.