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Despite the maturity of modern speaker verification technology, itsperformance still significantly degrades when facing non-neutrally-phonated(e.g., shouted and whispered) speech. To address this issue, in this paper, wepropose a new speaker embedding compensation method based on a minimum meansquare error (MMSE) estimator. This method models the joint distribution of thevocal effort transfer vector and non-neutrally-phonated embedding spaces andoperates in a principal component analysis domain to cope withnon-neutrally-phonated speech data scarcity. Experiments are carried out usinga cutting-edge speaker verification system integrating a powerfulself-supervised pre-trained model for speech representation. In comparison witha state-of-the-art embedding compensation method, the proposed MMSE estimatoryields superior and competitive equal error rate results when tackling shoutedand whispered speech, respectively.

Improved Vocal Effort Transfer Vector Estimation for Vocal Effort-Robust Speaker Verification