论文标题
隐藏网络：端到端的神经网络，用于深度语音情感识别中的数据包丢失隐藏
ConcealNet: An End-to-end Neural Network for Packet Loss Concealment in Deep Speech Emotion Recognition
论文作者
论文摘要
数据包丢失是数据传输中的一个常见问题，包括语音数据传输。这可能会影响流传输音频数据的广泛应用，例如流媒体应用程序或语音情感识别（SER）。数据包丢失隐藏（PLC）是任何面对数据包丢失的技术。简单的PLC基准是0-替代或线性插值。在本文中，我们提出了一个隐藏包装，可以与堆叠的复发神经细胞一起使用。隐藏单元可以提供一个经常性的神经网络（隐藏网络），该网络在推理时执行实时逐步端到端PLC。此外，通过端到端的情感预测神经网络扩展这一点，提供了一个网络，该网络通过端到端的框架丢失的音频执行SER。比较提出的模型与预先提到的基线。此外，利用具有更好性能的双向变体。为了进行评估，我们选择了公共Recola数据集，鉴于其具有连续的情感标签的长音轨。在此之后预测的相应情绪的重建以及相应的情绪质量上，对隐藏网络进行了评估。在音频重建和相应的情绪预测的环境中，即使损失经常发生，拟议的隐藏网模型在音频重建和相应的情绪预测中都显示出很大的改善。
Packet loss is a common problem in data transmission, including speech data transmission. This may affect a wide range of applications that stream audio data, like streaming applications or speech emotion recognition (SER). Packet Loss Concealment (PLC) is any technique of facing packet loss. Simple PLC baselines are 0-substitution or linear interpolation. In this paper, we present a concealment wrapper, which can be used with stacked recurrent neural cells. The concealment cell can provide a recurrent neural network (ConcealNet), that performs real-time step-wise end-to-end PLC at inference time. Additionally, extending this with an end-to-end emotion prediction neural network provides a network that performs SER from audio with lost frames, end-to-end. The proposed model is compared against the fore-mentioned baselines. Additionally, a bidirectional variant with better performance is utilised. For evaluation, we chose the public RECOLA dataset given its long audio tracks with continuous emotion labels. ConcealNet is evaluated on the reconstruction of the audio and the quality of corresponding emotions predicted after that. The proposed ConcealNet model has shown considerable improvement, for both audio reconstruction and the corresponding emotion prediction, in environments that do not have losses with long duration, even when the losses occur frequently.
