基于核融合扩张卷积神经网络的语音情感突发检测
Affective Burst Detection from Speech using Kernel-fusion Dilated Convolutional Neural Networks
摘要：
随着语音接口的日益丰富和普及，语音情感识别的应用前景越来越广阔。在连续情感识别（CER）问题中，跟踪情感状态的变化是一个重要的和期望的能力。尽管CER研究在评估中广泛使用相关性度量，但这些度量并不总是捕获情感领域中的所有高强度变化。在本文中，我们定义了一个新的情感突发检测问题，以准确地捕捉高强度的变化的情感属性。对于这个问题，我们制定了两个类的分类方法，隔离情感状态轮廓的情感爆发区域。该分类器是一个核融合扩张卷积神经网络（KFDCNN）架构驱动的语音频谱特征分割成空闲和突发部分的情感属性轮廓。在RECOLA和CreativeIT数据集上进行实验评估。在两个数据集上，观察到建议的KFDCNN优于基线前馈神经网络。
英文摘要：
As speech-interfaces are getting richer and widespread, speech emotion recognition promises more attractive applications. In the continuous emotion recognition (CER) problem, tracking changes across affective states is an important and desired capability. Although CER studies widely use correlation metrics in evaluations, these metrics do not always capture all the high-intensity changes in the affective domain. In this paper, we define a novel affective burst detection problem to accurately capture high-intensity changes of the affective attributes. For this problem, we formulate a two-class classification approach to isolate affective burst regions over the affective state contour. The proposed classifier is a kernel-fusion dilated convolutional neural network (KFDCNN) architecture driven by speech spectral features to segment the affective attribute contour into idle and burst sections. Experimental evaluations are performed on the RECOLA and CreativeIT datasets. The proposed KFDCNN is observed to outperform baseline feedforward neural networks on both datasets.
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。
