In this paper, a novel filtering method in feature extraction of speech is proposed for text-independent speaker identification, called Contextual Principal Curves Filtering (CPCF). The CPCF provides a good nonlinear summary of a sequence of cepstral vectors on the time context and, the most important, keeps their intrinsic trajectory characteristics, so the CPCF algorithm do improve the cepstr...

The purpose of this work is to show how recent developments in cepstral-based systems for speaker recognition can be leveraged for the use of Maximum Likelihood Linear Regression (MLLR) transforms. Speaker recognition systems based on MLLR transforms have shown to be greatly beneficial in combination with standard systems, but most of the advances in speaker modeling techniques have been implem...

This paper describes a series of cepstral-based compensation procedures that render the SPHINX-II system more robust with respect to acoustical changes in the environment. The first algorithm, RATZ (MultivaRiate gAussian based cepsTral normaliZation) requires stereo-data for computing compensation terms, and is similar in philosophy to MFCDCN [ref] (in fact MFCDCN can be thought of as a discret...

Maximum-Likelihood Linear Regression (MLLR) and Constrained MLLR (CMLLR) have been recently used for feature extraction in speaker recognition. These systems use (C)MLLR transforms as features that are modeled with Support Vector Machines (SVM). This paper evaluates and compares several of these approaches for the NIST Speaker Recognition task. Single CMLLR and up to 4-phonetic-class MLLR trans...