Abstract. This paper proposes the use of Maximum A Posteriori Linear Regression (MAPLR) transforms as feature for language recognition. Rather than estimating ...

This paper proposes the use of Maximum A Posteriori Linear Regression (MAPLR) transforms as feature for language recognition. Rather than estimating the ...

In this paper, the theoretical framework of maximum a posteriori linear regression (MAPLR) based variance adaptation for continuous density HMMs is ...

This work is enriched by a proposed multi-class technique, which clusters the Gaussian mixtures into regression classes and estimates a different transform for ...

ABSTRACT. In this paper, a new approach for model adaptation, extended maximum a posterior linear regression (EMAPLR), is described and studied.

An efficient method for speaker adaptation (SA) is proposed that tries to estimate the transformations by maximum a posteriori (MAP) criterion and the ...

Sep 14, 2014 · We propose a feature space maximum a posteriori (MAP) linear regression framework to adapt parameters for context depen-.

ABSTRACT. Transformation-based model adaptation techniques like maxi- mum likelihood linear regression (MLLR) rely on an accurate se-.

ABSTRACT. Recently, using maximum likelihood linear regression (MLLR) transforms as the features for SVM based speaker recognition has been proposed.

Bibliographic details on Maximum A Posteriori Linear Regression for language recognition.