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首页> 外文会议>International Conference on Optical Communications and Networks >Speech enhancement with stacked frames and deep neural network for VoIP applications
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Speech enhancement with stacked frames and deep neural network for VoIP applications

机译：具有堆叠帧和VoIP应用的深神经网络的语音增强

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摘要

Speech enhancement is a critical part of variety types of communication systems and automatic speech recognition (ASR) applications. In this study we propose a speech enhancement method for real time VoIP applications with stacked frames and deep neural network, a novel data preparation approach is also introduced. In contrast to many states of art learning-based method, we focused on real-time implement in VoIP applications. Experiments were conducted on speech degraded by different noise types and SNR levels which were not seen in the training stage of the deep neural network and achieved a significant improvement on PESQ. Important traditional real-time speech enhancement method and most recent states of art learning-based method were also tested and compared with proposed method. The results show that proposed method effectively improve the speech intelligibility, greatly outperform traditional real-time minimum-mean square error (MMSE) algorithm and real-time learning-based CNN method in PESQ. We also achieve comparable PESQ in comparison with most recent state of the art learning-based method, but outperform it in time complexity. Making this method attractive in VoIP communication system applications which is high demand on communication latency.
机译：语音增强是各种通信系统和自动语音识别（ASR）应用的关键部分。在这项研究中，我们提出了一种语音增强方法，用于使用堆叠帧和深神经网络的实时VoIP应用，还引入了一种新的数据准备方法。与许多基于艺术学习的方法相比，我们专注于VoIP应用中的实时工具。通过在深神经网络的训练阶段中未见的不同噪声类型和SNR水平的言语降解进行实验，并对PESQ进行了显着改善。重要的传统实时语音增强方法和最近的基于艺术学习方法的最新状态也与提出的方法进行了测试。结果表明，提出的方法有效地提高了语音可懂度，大大优先突出的传统实时最小均值误差（MMSE）算法和基于PESQ的实时学习的CNN方法。与基于艺术学习的方法的最新状态相比，我们也实现了可比的PESQ，但在时间复杂性上表现优于差异。使该方法在VoIP通信系统应用中具有吸引力，这对通信延迟的需求很高。

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