Personalized speech enhancement (PSE) is a real-time SE approach utilizing a speaker embedding of a target person to remove background noise, reverberation, and interfering voices. To deploy a PSE model for full duplex communications, the model must be combined with acoustic echo cancellation (AEC), although such a combination has been less explored. This paper proposes a series of methods that are applicable to various model architectures to develop efficient causal models that can handle the tasks of PSE, AEC, and joint PSE-AEC. We present extensive evaluation results using both simulated data and real recordings, covering various acoustic conditions and evaluation metrics. The results show the effectiveness of the proposed methods for two different model architectures. Our best joint PSE-AEC model comes close to the expert models optimized for individual tasks of PSE and AEC in their respective scenarios and significantly outperforms the expert models for the combined PSE-AEC task.

翻译：个性化语音增强（PSE）是一种利用目标说话人语音嵌入进行实时语音增强的方法，旨在消除背景噪声、混响及干扰语音。为在全双工通信中部署PSE模型，需将其与声学回声消除（AEC）技术相结合，然而此类联合方案的研究尚不充分。本文提出一系列适用于多种模型架构的方法，用于开发能够高效处理PSE、AEC及联合PSE-AEC任务的因果模型。我们通过模拟数据和真实录音开展了涵盖多种声学条件与评估指标的广泛评测。结果表明，所提方法在两种不同模型架构上均有效。最优联合PSE-AEC模型在各自场景下接近针对PSE和AEC单项任务优化的专家模型，且在联合PSE-AEC任务中显著超越专家模型。