Department of Electronic Systems, Aalborg University, Denmark
Center for Robust Speech Systems (CRSS), The University of Texas at Dallas, USA
VeriDas | das-Nano, Navarre, Spain
ViVoLab, Aragón Institute for Engineering Research (I3A), University of Zaragoza, Spain
Contact: {ortega, lleida}@unizar.es
doi: 10.1109/MLSP55844.2023.10285923
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 nonneutrally-phonated embedding spaces and operates in a principal component analysis domain to cope with non-neutrallyphonated 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.
