Probabilistic Bilinear Transformation Space-Based Joint Maximum A Posteriori Adaptation

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Joint Bilinear Transformation Space Based Maximum a posteriori Linear Regression Adaptation Using Prior with Variance Function

This paper proposes a new joint maximum a posteriori linear regression (MAPLR) adaptation using single prior distribution with a variance function in bilinear transformation space (BITS). There are two indirect adaptation methods based on the linear transformation in BITS and these are tightly coupled by joint MAP-based estimation. The proposed method not only has the scalable parameters but al...

متن کامل

Bilinear transformation space-based maximum likelihood linear regression frameworks

This paper proposes two types of bilinear transformation spacebased speaker adaptation frameworks. In training session, transformation matrices for speakers are decomposed into the style factor for speakers’ characteristics and orthonormal basis of eigenvectors to control dimensionality of the canonical model by the singular value decomposition-based algorithm. In adaptation session, the style ...

متن کامل

Maximum a Posteriori Model Adaptation

In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future....

متن کامل

Maximum A Posteriori Adaptation of HMM Parameters Based on Probabilistic Principle Component Analysis

In this paper, we propose a new approach to hidden Markov model (HMM) adaptation based on the probabilistic principle component analysis (PPCA). The proposed approach has been developed to adapt not only the HMM means but also the variances and mixture weights simultaneously. Due to a set of constraints, we apply the PPCA model in the transformed domain where we adapt the variance and mixture w...

متن کامل

Maximum a posteriori adaptation of HMM parameters based on speaker space projection

This paper presents a novel approach to rapid speaker adaptation based on the speaker space projection paradigm in which the adapted model is constrained to lie on a specific subspace spanned by a small number of basis vectors. In order to select the basis vectors that form the speaker space, we apply probabilistic principal component analysis (PPCA) technique to a set of training speaker model...

متن کامل

ذخیره 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: ETRI Journal

سال: 2012

ISSN: 1225-6463

DOI: 10.4218/etrij.12.0212.0054