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.

翻译：尽管现代说话人确认技术已日趋成熟，但在处理非中性发声（如喊叫与耳语）语音时，其性能仍会显著下降。为解决这一问题，本文提出一种基于最小均方误差（MMSE）估计器的说话人嵌入补偿新方法。该方法通过对嗓音力度转移向量与非中性发声嵌入空间的联合分布进行建模，并在主成分分析域中实施操作，以应对非中性发声语音数据稀缺的挑战。实验采用集成强大自监督预训练模型的先进说话人确认系统进行。与现有最优嵌入补偿方法相比，所提出的MMSE估计器在处理喊叫语音与耳语语音时，分别实现了更优且具有竞争力的等错误率结果。