论文标题
实时联合个性化语音增强和声学回声取消
Real-Time Joint Personalized Speech Enhancement and Acoustic Echo Cancellation
论文作者
论文摘要
个性化的语音增强（PSE）是一种实时的SE方法，利用目标人员嵌入的扬声器来消除背景噪音，混响和干扰声音。要为完整的双工通信部署PSE模型，该模型必须与声学回声取消（AEC）结合使用，尽管这种组合的探索较少。本文提出了一系列适用于各种模型架构的方法，以开发可以处理PSE，AEC和联合PSE-AEC任务的有效因果模型。我们使用模拟数据和真实记录提出了广泛的评估结果，涵盖了各种声学条件和评估指标。结果表明，提出的方法对两个不同模型架构的有效性。我们最佳的PSE-AEC模型与在各自的方案中针对PSE和AEC的各个任务优化的专家模型接近，并大大优于合并的PSE-AEC任务的专家模型。
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.
