Analysis and utilization of MLLR speaker adaptation technique for learners' pronunciation evaluation
Dean Luo, Yu Qiao, Nobuaki Minematsu, Yutaka Yamauchi, Keikichi Hirose · 2009
In this paper, we investigate the effects and problems of MLLR speaker adaptation when applied to pronunciation evaluation. Automatic scoring and error detection experiments are conducted on two publicly available databases of Japanese learners’ English pronunciation. As we expected, overadaptation causes misjudge of pronunciation accuracy. Following these experiments, two novel methods, Forced-aligned GOP scoring and Regularized-MLLR adaptation, are proposed to solve the adverse effects of MLLR adaption. Experimental results show that the proposed methods can better utilize MLLR adaptation and avoid over-adaptation. Index Terms: Computer Assisted Language Learning (CALL), speaker adaption, pronunciation evaluation, goodness of pronunciation (GOP), maximum likelihood linear regression (MLLR)