  Despite the maturity of modern speaker verification technology, its
performance still significantly degrades when facing non-neutrally-phonated
(e.g., shouted and whispered) speech. To address this issue, in this paper, we
propose a new speaker embedding compensation method based on a minimum mean
square error (MMSE) estimator. This method models the joint distribution of the
vocal effort transfer vector and non-neutrally-phonated embedding spaces and
operates in a principal component analysis domain to cope with
non-neutrally-phonated speech data scarcity. Experiments are carried out using
a cutting-edge speaker verification system integrating a powerful
self-supervised pre-trained model for speech representation. In comparison with
a state-of-the-art embedding compensation method, the proposed MMSE estimator
yields superior and competitive equal error rate results when tackling shouted
and whispered speech, respectively.
