Personalized speech enhancement (PSE) models can improve the audio quality of
teleconferencing systems by adapting to the characteristics of a speaker's
voice. However, most existing methods require a separate speaker embedding
model to extract a vector representation of the speaker from enrollment audio,
which adds complexity to the training and deployment process. We propose to use
the internal representation of the PSE model itself as the speaker embedding,
thereby avoiding the need for a separate model. We show that our approach
performs equally well or better than the standard method of using a pre-trained
speaker embedding model on noise suppression and echo cancellation tasks.
Moreover, our approach surpasses the ICASSP 2023 Deep Noise Suppression
Challenge winner by 0.15 in Mean Opinion Score.

个性化语音增强模型通过适应说话者的语音特征，可以提高远程会议系统的音频质量。然而，大多数现有方法需要独立的说话者嵌入模型从注册音频中提取说话者的向量表示，这增加了训练和部署过程的复杂性。我们提出使用个性化语音增强模型本身的内部表示作为说话者嵌入，从而避免了需要独立模型的需求。我们证明我们的方法在降噪和消除回声任务上表现出与使用预训练的说话者嵌入模型的标准方法相同或更好的效果。此外，我们的方法在 2023 年 ICASSP 深度噪声抑制挑战中比冠军的平均观点分高 0.15。