Smoking Action Recognition Based on Spatial-Temporal Convolutional Neural Networks

Chiu, Chien-Fang; Kuo, Chin-Hwa; Chang, Pao-Chi

doi:10.23919/apsipa.2018.8659703

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…At present, the application of graph neural networks in the field of speech technology still has some limitations [25], but some scholars have verified the advantages of graph convolution in the field of speech technology and the possibility of being widely used through research, such as conversational speech recognition [26], sentence-level [27] / conversation-level speech emotion recognition [28], speech enhancement [29], and Q &A rewriting [30]. The methods of graph construction can be divided into sample point-based, frame-based, speech channel-based, and historical dialogue-based approaches, as shown in Fig.…”

Section: Ser Based On Gnnsmentioning

confidence: 99%

Speech emotion recognition based on Graph-LSTM neural network

Li,

Wang,

Yang

et al. 2023

J AUDIO SPEECH MUSIC PROC.

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Currently, Graph Neural Networks have been extended to the field of speech signal processing. It is the more compact and flexible way to represent speech sequences by graphs. However, the structures of the relationships in recent studies are tend to be relatively uncomplicated. Moreover, the graph convolution module exhibits limitations that impede its adaptability to intricate application scenarios. In this study, we establish the speech-graph using feature similarity and introduce a novel architecture for graph neural network that leverages an LSTM aggregator and weighted pooling. The unweighted accuracy of 65.39% and the weighted accuracy of 71.83% are obtained on the IEMOCAP dataset, achieving the performance comparable to or better than existing graph baselines. This method can improve the interpretability of the model to some extent, and identify speech emotion features effectively.

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