Regularized Maximum Likelihood Linear Regression Adaptation for Computer-Assisted Language Learning Systems

Regularized-MLLR speaker adaptation for computer-assisted language learning system

In this paper, we propose a novel speaker adaptation technique, regularized-MLLR, for Computer Assisted Language Learning (CALL) systems. This method uses a linear combination of a group of teachers’ transformation matrices to represent each target learner’s transformation matrix, thus avoids the over-adaptation problem that erroneous pronunciations come to be judged as good pronunciations afte...

Regularized feature-based maximum likelihood linear regression for speech recognition

In many automatic speech recognition (ASR) applications, maximum likelihood linear regression (MLLR), and feature-based maximum likelihood linear regression (FMLLR) are used for speaker adaptation. This paper investigates a possible generalization of FMLLR which addresses the degradation in the performance of ASR systems due to small —possibly time-varying— perturbations of the training and the...

Speaker Adaptation using Maximum Likelihood Linear Regression

Constrained maximum likelihood linear regression for speaker adaptation

This paper proposes a new structure for use in MLLR adaptation aiming at constraining the transform for potentially better parameter estimation from sparse adaptation data. Motivations for the use of the new structure, and EM based parameter estimation are presented. Experimental results on Spoke3 of the Wall Street Journal task revealed that the proposed transformations outperform a full matri...

Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation

Multilingual Automatic Speech Recognition (ASR) systems are of great interest in multilingual environments. We studied the case of the Comunitat Valenciana where the two official languages are Spanish and Valencian. These two languages share most of their phonemes, and their syntax and vocabulary are also quite similar since they have influenced each other for many years. We constructed a syste...