Speaker adaptation in transformation space using two-dimensional PCA
نویسندگان
چکیده
This paper describes a principled application of twodimensional principal component analysis (2DPCA) to the decomposition of transformation matrices of maximum likelihood linear regression (MLLR) and its application to speaker adaptation using the bases derived from the analysis. Our previous work applied 2DPCA to speaker-dependent (SD) models to obtain the bases for state space. In this work, we apply 2DPCA to a set of MLLR transformation matrices of training speakers to obtain the bases for transformation space, since the matrices are 2-D in nature, and 2DPCA can decompose a set of matrices without vectorization. Here, we present two approaches using 2DPCA: One in eigenspace-based MLLR (ES-MLLR) framework and the other one in maximum a posteriori linear regression (MAPLR) framework. The experimental results showed that the proposed methods outperformed ES-MLLR for the adaptation data of about 10 seconds or longer.
منابع مشابه
Improving eigenspace-based MLLR adaptation by kernel PCA
Eigenspace-based MLLR (EMLLR) adaptation has been shown effective for fast speaker adaptation. It applies the basic idea of eigenvoice adaptation, and derives a small set of eigenmatrices using principal component analysis (PCA). The MLLR adaptation transformation of a new speaker is then a linear combination of the eigenmatrices. In this paper, we investigate the use of kernel PCA to find the ...
متن کاملFast speaker adaptation using eigenspace-based maximum likelihood linear regression
This paper presents an eigenspace-based fast speaker adaptation approach which can improve the modeling accuracy of the conventional maximum likelihood linear regression (MLLR) techniques when only very limited adaptation data is available. The proposed eigenspace-based MLLR approach was developed by introducing a priori knowledge analysis on the training speakers via PCA, so as to construct an...
متن کاملA comparative study of two kernel eigenspace-based speaker adaptation methods on large vocabulary continuous speech recognition
Eigenvoice (EV) speaker adaptation has been shown effective for fast speaker adaptation when the amount of adaptation data is scarce. In the past two years, we have been investigating the application of kernel methods to improve EV speaker adaptation by exploiting possible nonlinearity in the speaker space, and two methods were proposed: embedded kernel eigenvoice (eKEV) and kernel eigenspace-b...
متن کاملWithin-class covariance normalization for SVM-based speaker recognition
This paper extends the within-class covariance normalization (WCCN) technique described in [1, 2] for training generalized linear kernels. We describe a practical procedure for applying WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. Our approach involves using principal component analysis (PCA) to split the original feature sp...
متن کاملSpeedup of kernel eigenvoice speaker adaptation by embedded kernel PCA
Recently, we proposed an improvement to the eigenvoice (EV) speaker adaptation called kernel eigenvoice (KEV) speaker adaptation. In KEV adaptation, eigenvoices are computed using kernel PCA, and a new speaker’s adapted model is implicitly computed in the kernel-induced feature space. Due to many online kernel evaluations, both adaptation and subsequent recognition of KEV adaptation are slower ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010