Speaker and Session Variability in GMM-Based Speaker Verification
نویسندگان
چکیده
منابع مشابه
SVM Speaker Verification Using Session Variability Modelling and GMM Supervectors
This paper demonstrates that modelling session variability during GMM training can improve the performance of a GMM supervector SVM speaker verification system. Recently, a method of modelling session variability in GMM-UBM systems has led to significant improvements when the training and testing conditions are subject to session effects. In this work, session variability modelling is applied d...
متن کاملAn orthogonal GMM based speaker verification system
This paper describes a new speaker verification system based on orthogonal Gaussian mixture modeling (GMM) techniques combined with maximum a posteriori (MAP) adaptation. In most of the GMM based speaker verification systems, the variance of each component is constrained to be diagonal for its computational simplicity. However, this approximation inevitably introduces performance degradation. T...
متن کاملLearning to boost GMM based speaker verification
The Gaussian mixture models (GMM) has proved to be an effective probabilistic model for speaker verification, and has been widely used in most of state-of-the-art systems. In this paper, we introduce a new method for the task: that using AdaBoost learning based on the GMM. The motivation is the following: While a GMM linearly combines a number of Gaussian models according to a set of mixing wei...
متن کاملModelling session variability in text-independent speaker verification
Presented is an approach to modelling session variability for GMM-based text-independent speaker verification incorporating a constrained session variability component in both the training and testing procedures. The proposed technique reduces the data labelling requirements and removes discrete categorisation needed by techniques such as feature mapping and H-Norm, while providing superior per...
متن کاملExplicit modelling of session variability for speaker verification
This article describes a general and powerful approach to modelling mismatch in speaker recognition by including an explicit session term in the Gaussian mixture speaker modelling framework. Under this approach, the Gaussian mixture model (GMM) that best represents the observations of a particular recording is the combination of the true speaker model with an additional session-dependent offset...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Audio, Speech and Language Processing
سال: 2007
ISSN: 1558-7916
DOI: 10.1109/tasl.2007.894527