论文标题
萨德尔：基于多任务学习的联合语音分离和denoising模型
SADDEL: Joint Speech Separation and Denoising Model based on Multitask Learning
论文作者
论文摘要
在实际情况下收集的语音数据通常遇到两个问题。首先，可以同时存在多个来源，并且来源的数量可能随时间而变化。其次，记录中背景噪声的存在是不可避免的。为了处理第一个问题，我们指的是语音分离方法，这些方法将语音与未知数量的说话者分开。为了解决第二个问题，我们指的是语音denoising方法，这些方法消除了噪声组件并检索纯语音信号。已经提出了许多基于深度学习的语音分离和降解方法，以显示出令人鼓舞的结果。但是，尽管语音分离和具有相似性质的任务，但很少有著作试图同时解决这些问题。在这项研究中，我们提出了基于多任务学习标准的联合语音分离和降级框架，以同时解决这两个问题。实验结果表明，所提出的框架不仅在语音分离和降解任务上都表现良好，而且在大多数条件下都优于相关方法。
Speech data collected in real-world scenarios often encounters two issues. First, multiple sources may exist simultaneously, and the number of sources may vary with time. Second, the existence of background noise in recording is inevitable. To handle the first issue, we refer to speech separation approaches, that separate speech from an unknown number of speakers. To address the second issue, we refer to speech denoising approaches, which remove noise components and retrieve pure speech signals. Numerous deep learning based methods for speech separation and denoising have been proposed that show promising results. However, few works attempt to address the issues simultaneously, despite speech separation and denoising tasks having similar nature. In this study, we propose a joint speech separation and denoising framework based on the multitask learning criterion to tackle the two issues simultaneously. The experimental results show that the proposed framework not only performs well on both speech separation and denoising tasks, but also outperforms related methods in most conditions.
