We present SVCnet, a system for modelling speaker variability. Encoder Neural
Networks specialized for each speech sound produce low dimensionality models of
acoustical variation, and these models are further combined into an overall
model of voice variability. A training procedure is described which minimizes
the dependence of this model on which sounds have been uttered. Using the
trained model (SVCnet) and a brief, unconstrained sample of a new speaker's
voice, the system produces a Speaker Voice Code that can be used to adapt a
recognition system to the new speaker without retraining. A system which
combines SVCnet with an MS-TDNN recognizer is described

该研究介绍了一种名为 SVCnet 的系统，使用专门针对每种语音声音的编码神经网络生成小维度的声学变异模型，并进一步将这些模型组合成总体的声音变异模型。该系统可以用于适应识别新说话人的功能，其中将 SVCnet 与 MS-TDNN 识别器相结合的系统也被详细介绍。

快速连接主义说话人适应

Rapid Connectionist Speaker Adaptation

Representation learning for speech emotion recognition is challenging due to
labeled data sparsity issue and lack of gold standard references. In addition,
there is much variability from input speech signals, human subjective
perception of the signals and emotion label ambiguity. In this paper, we
propose a machine learning framework to obtain speech emotion representations
by limiting the effect of speaker variability in the speech signals.
Specifically, we propose to disentangle the speaker characteristics from
emotion through an adversarial training network in order to better represent
emotion. Our method combines the gradient reversal technique with an entropy
loss function to remove such speaker information. Our approach is evaluated on
both IEMOCAP and CMU-MOSEI datasets. We show that our method improves speech
emotion classification and increases generalization to unseen speakers.

本文提出了一种基于对抗训练网络来消除发言人特征并更好地表示情感的机器学习框架，在 IEMOCAP 和 CMU-MOSEI 数据集上实验表明该框架能够提高语音情感分类的准确性并增加对未知发言人的泛化能力。