Multi-Stage Graph Representation Learning for Dialogue-Level Speech Emotion Recognition
Author:
Affiliation:
1. Tianjin University,Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing,Tianjin,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09746237.pdf?arnumber=9746237
Reference20 articles.
1. Efficient emotion recognition from speech using deep learning on spectrograms;satt;InterSpeech,2017
2. Long Short-Term Memory-Networks for Machine Reading
3. DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
4. Convolutional neural networks on graphs with fast localized spectral filtering;defferrard;Advances in neural information processing systems,2016
5. Dialoguegcn: A graph convolutional neural network for emotion recognition in conversation;ghosal,2020
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1. Speech emotion recognition based on Graph-LSTM neural network;EURASIP Journal on Audio, Speech, and Music Processing;2023-10-11
2. Speech Topic Classification Based on Pre-trained and Graph Networks;2023 IEEE International Conference on Multimedia and Expo (ICME);2023-07
3. Multi-Scale Receptive Field Graph Model for Emotion Recognition in Conversations;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04
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