Cleft palate is one of common congenital malformations that has huge impacts on the physical and psychological health of patients. Pharyngeal fricative in cleft palate speech is a kind of common compensatory articulations, which is produced by retracting tongue position to posterior pharyngeal wall and narrowing velopharyngeal opening. In this paper, based on voice mechanism of pharyngeal fricatives in cleft palate speech, linear prediction inverse filter was used to extract glottal waveform and estimate vocal tract model coefficients. Then four features were extracted, including pitch period, glottal flow derivative waveform, vocal tract area and vocal tract gain. The cross-correlation function was used to calculate the correlation between features. Glottal flow derivative waveform was removed since it had strong correlation with the others. KNN classifier was applied to realize the automatic pharyngeal fricatives detection in cleft palate speech, which reaches the detection accuracy of 98.40%.

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Temporal networks are networks that edges evolve over time, hence link prediction in temporal networks aims at inferring new edges based on a sequence of network snapshots. In this paper, we propose a graph wavelet neural network (TT-GWNN) framework using topological and temporal features for link prediction in temporal networks. To capture topological and temporal features, we develope a second-order weighted random walk sampling algorithm. It combines network snapshots with both first-order and second-order weights into one weighted graph. Moreover, it incorporates a damping factor to assign greater weights to more recent snapshots. Next, we adopt graph wavelet neural networks to embed the vertices and use gated recurrent units for predicting new links. Extensive experiments demonstrate that TT-GWNN can effectively predict links on temporal networks.

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Temporal networks are networks that edges evolve over time. Network embedding is an important approach that aims at learning lowdimension latent representations of nodes while preserving the spatialtemporal features for temporal network analysis. In this paper, we propose a spatial-temporal higher-order graph convolutional network framework (ST-HN) for temporal network embedding. To capture spatialtemporal features, we develop a truncated hierarchical random walk sampling algorithm (THRW), which randomly samples the nodes from the current snapshot to the previous one. To capture hierarchical attributes, we improve upon the state-of-the-art approach, higher-order graph convolutional architectures, to be able to aggregate spatial features of different hops and temporal features of different timestamps with weight, which can learn mixed spatial-temporal feature representations of neighbors at various hops and snapshots and can well preserve the evolving behavior hierarchically. Extensive experiments on link prediction demonstrate the effectiveness of our model.