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SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech
13 references·4 citing·45 second hop
SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech (2022)SpeechFormer: A Hierarchical Efficient Fr…Key-Sparse Transformer for Multimodal Speech Emotion Recognition (2022)Key-Sparse Transformer for Mu…CTNet: Conversational Transformer Network for Emotion Recognition (2021)CTNet: Conversational Transfo…Semi-supervised Multi-modal Emotion Recognition with Cross-Modal Distribution Matching (2020)Semi-supervised Multi-modal E…MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (2019)MELD: A Multimodal Multi-Part…Automatic speech emotion recognition using recurrent neural networks with local attention (2017)Automatic speech emotion reco…Decision Tree Based Depression Classification from Audio Video and Language Information (2016)Decision Tree Based Depressio…AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge (2016)AVEC 2016 - Depression, Mood,…High-level feature representation using recurrent neural network for speech emotion recognition (2015)High-level feature representa…The Distress Analysis Interview Corpus of human and computer interviews (2014)The Distress Analysis Intervi…Deep neural networks for acoustic emotion recognition: Raising the benchmarks (2011)Deep neural networks for acou…IEMOCAP: interactive emotional dyadic motion capture database (2008)IEMOCAP: interactive emotiona…Hidden Markov model-based speech emotion recognition (2003)Hidden Markov model-based spe…A Stochastic Approximation Method (1985)A Stochastic Approximation Me…Text-guided multimodal depression detection via cross-modal feature reconstruction and decomposition (2024)Text-guided multimodal depres…Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data (2024)Multimodal Sensing for Depres…Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement (2023)Enhancing accuracy and privac…DST: Deformable Speech Transformer for Emotion Recognition (2023)DST: Deformable Speech Transf…A database of German emotional speech (2005)The eNTERFACE’05 Audio-Visual Emotion Database (2006)The INTERSPEECH 2009 emotion challenge (2009)Paralinguistics in speech and language—State-of-the-art and the challenge (2012)Attentive Convolutional Neural Network Based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech (2017)End-to-End Speech Emotion Recognition Using Deep Neural Networks (2018)Coefficient Alpha and the Internal Structure of Tests (1951)The Hidden Dimension (1973)Affective Computing (1997)Speech emotion recognition using deep 1D & 2D CNN LSTM networks (2018)Manual for the State-Trait Anxiety Inventory (1970)The PHQ-9: A New Depression Diagnostic and Severity Measure (2002)Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. 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End-to-end speech emotion recognition using a deep convolutional recurrent network (2016)Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019)Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers (2019)A systematic review on affective computing: emotion models, databases, and recent advances (2022)Measuring emotion: The self-assessment manikin and the semantic differential (1994)A review of affective computing: From unimodal analysis to multimodal fusion (2017)DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (2019)COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020)The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2015)A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023)Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: A systematic review of recent advancements and future prospects (2023)Emotion recognition in human-computer interaction (2001)Speech emotion recognition based on HMM and SVM (2005)Affective State Estimation for Human–Robot Interaction (2007)Acoustic feature selection for automatic emotion recognition from speech (2008)An Online Convex Optimization Approach to Proactive Network Resource Allocation (2017)Is Deep Learning for Image Recognition Applicable to Stock Market Prediction? (2019)Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild (2023)
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References

  • 2008IEMOCAP: interactive emotional dyadic motion capture database3,864 cited
  • 2019MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations1,413 cited
  • 2017Automatic speech emotion recognition using recurrent neural networks with local attention769 cited
  • 2014The Distress Analysis Interview Corpus of human and computer interviews591 cited
  • 2015High-level feature representation using recurrent neural network for speech emotion recognition406 cited
  • 2021CTNet: Conversational Transformer Network for Emotion Recognition299 cited
  • 2011Deep neural networks for acoustic emotion recognition: Raising the benchmarks288 cited
  • 1985A Stochastic Approximation Method284 cited
  • 2003Hidden Markov model-based speech emotion recognition278 cited
  • 2016Decision Tree Based Depression Classification from Audio Video and Language Information148 cited
  • 2016AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge104 cited
  • 2022Key-Sparse Transformer for Multimodal Speech Emotion Recognition89 cited
  • 2020Semi-supervised Multi-modal Emotion Recognition with Cross-Modal Distribution Matching73 cited

Cited by

  • 2024Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data

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68 cited
  • 2024Text-guided multimodal depression detection via cross-modal feature reconstruction and decomposition50 cited
  • 2023DST: Deformable Speech Transformer for Emotion Recognition45 cited
  • 2023Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement41 cited