Mixture Linear Prediction in Speaker Verification Under Vocal Effort Mismatch

Abstract

This paper describes an approach to robust signal analysis using iterative parameter re-estimation of a mixture autoregressive (AR) model. The model's focus can be adjusted by initialization of the target and non-target states. The variant examined in this study uses an i.i.d. mixture AR model and is designed to tackle the spectral biasing effect caused by the voice excitation in speech signals with variable fundamental frequency. In our speaker verification experiments, this method performed competitively against standard spectrum analysis techniques in non-mismatch conditions and showed significant improvements in vocal effort mismatch conditions.