MLNET：一种用于语音活动检测的自适应多感受场注意神经网络
MLNET: An Adaptive Multiple Receptive-field Attention Neural Network for Voice Activity Detection
摘要：
语音活动检测（VAD）区分语音和非语音，其性能对于基于语音的服务至关重要。最近，基于深度神经网络（DNN）的VAD已经实现了比传统信号处理方法更好的性能。现有的基于DNN的语音检测模型通常采用手工创建固定窗口的方法来利用上下文语音信息来提高语音检测的性能。然而，固定的上下文语音信息窗口不能处理各种不可预测的噪声环境，突出关键的语音信息VAD任务。为了解决这一问题，本文提出了一种自适应多感受野注意神经网络（MLNET）来完成VAD任务。MLNET利用多分支提取多个上下文语音信息，并研究了有效的注意力块来加权上下文的最关键部分以进行最终分类。在真实场景中的实验表明，所提出的基于MLNET的模型优于其他基线。
英文摘要：
Voice activity detection (VAD) makes a distinction between speech and non-speech and its performance is of crucial importance for speech based services. Recently, deep neural network (DNN)-based VADs have achieved better performance than conventional signal processing methods. The existed DNNbased models always handcrafted a fixed window to make use of the contextual speech information to improve the performance of VAD. However, the fixed window of contextual speech information can't handle various unpredicatable noise environments and highlight the critical speech information to VAD task. In order to solve this problem, this paper proposed an adaptive multiple receptive-field attention neural network, called MLNET, to finish VAD task. The MLNET leveraged multi-branches to extract multiple contextual speech information and investigated an effective attention block to weight the most crucial parts of the context for final classification. Experiments in real-world scenarios demonstrated that the proposed MLNET-based model outperformed other baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
