Year: 2023
Type: article
Abstract: 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.
Authors: Yan Li, Yapeng Wang, Xu Yang, Sio-Kei Im
Institution: Macao Polytechnic University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,635
OpenAlex ID: W4387530839