On the public citation graph

https://doi.org/10.2139/ssrn.4647924

0 signed · 62 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00779-014-0777-0openalex-extracted
EmoSnaps: a mobile application for emotion recall from facial expressions
inferredcites →https://doi.org/10.1007/s10462-021-09958-2openalex-extracted
Transformer models for text-based emotion detection: a review of BERT-based approaches
inferredcites →https://doi.org/10.1007/s10579-008-9076-6openalex-extracted
IEMOCAP: interactive emotional dyadic motion capture database
inferredcites →https://doi.org/10.1016/j.eswa.2020.114382openalex-extracted
Towards Emotion-aware Recommender Systems: an Affective Coherence Model based on Emotion-driven Behaviors
inferredcites →https://doi.org/10.1016/j.neucom.2021.02.094openalex-extracted
Spatiotemporal and frequential cascaded attention networks for speech emotion recognition
inferredcites →https://doi.org/10.1016/j.specom.2020.12.001openalex-extracted
Fusion of deep learning features with mixture of brain emotional learning for audio-visual emotion recognition
inferredcites →https://doi.org/10.1016/j.specom.2020.12.009openalex-extracted
Learning deep multimodal affective features for spontaneous speech emotion recognition
inferredcites →https://doi.org/10.1016/j.specom.2022.02.006openalex-extracted
Multi-modal speech emotion recognition using self-attention mechanism and multi-scale fusion framework
inferredcites →https://doi.org/10.1038/s41467-022-30761-2openalex-extracted
Towards artificial general intelligence via a multimodal foundation model
inferredcites →https://doi.org/10.1109/acii.2015.7344669openalex-extracted
An experimental study of speech emotion recognition based on deep convolutional neural networks
inferredcites →https://doi.org/10.1109/cvpr.2006.100openalex-extracted
Dimensionality Reduction by Learning an Invariant Mapping
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00975openalex-extracted
Momentum Contrast for Unsupervised Visual Representation Learning
inferredcites →https://doi.org/10.1109/cvpr52688.2022.02057openalex-extracted
M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
inferredcites →https://doi.org/10.1109/icassp.2017.7952552openalex-extracted
Automatic speech emotion recognition using recurrent neural networks with local attention
inferredcites →https://doi.org/10.1109/icassp39728.2021.9414314openalex-extracted
A Novel end-to-end Speech Emotion Recognition Network with Stacked Transformer Layers
inferredcites →https://doi.org/10.1109/icassp40776.2020.9053012openalex-extracted
Gated Mechanism for Attention Based Multi Modal Sentiment Analysis
inferredcites →https://doi.org/10.1109/icassp43922.2022.9746598openalex-extracted
Key-Sparse Transformer for Multimodal Speech Emotion Recognition
inferredcites →https://doi.org/10.1109/icassp43922.2022.9746924openalex-extracted
Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?
inferredcites →https://doi.org/10.1109/icassp49357.2023.10095855openalex-extracted
MGAT: Multi-Granularity Attention Based Transformers for Multi-Modal Emotion Recognition
inferredcites →https://doi.org/10.1109/icassp49357.2023.10096709openalex-extracted
Exploring Complementary Features in Multi-Modal Speech Emotion Recognition
inferredcites →https://doi.org/10.1109/icassp49357.2023.10097110openalex-extracted
Multilevel Transformer for Multimodal Emotion Recognition
inferredcites →https://doi.org/10.1109/iccwamtip51612.2020.9317379openalex-extracted
Comparative Analyses of Bert, Roberta, Distilbert, and Xlnet for Text-Based Emotion Recognition
inferredcites →https://doi.org/10.1109/jstsp.2022.3188113openalex-extracted
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
inferredcites →https://doi.org/10.1109/taffc.2022.3164516openalex-extracted
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition
inferredcites →https://doi.org/10.1109/taffc.2023.3243463openalex-extracted
Cluster-Level Contrastive Learning for Emotion Recognition in Conversations
inferredcites →https://doi.org/10.1109/taslp.2017.2759338openalex-extracted
Semisupervised Autoencoders for Speech Emotion Recognition
inferredcites →https://doi.org/10.1109/taslp.2021.3122291openalex-extracted
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
inferredcites →https://doi.org/10.1109/taslp.2022.3171965openalex-extracted
ISNet: Individual Standardization Network for Speech Emotion Recognition
inferredcites →https://doi.org/10.1109/tcsvt.2023.3247822openalex-extracted
Semantic Alignment Network for Multi-Modal Emotion Recognition
inferredcites →https://doi.org/10.1145/3194452.3194460openalex-extracted
Improvement on Speech Emotion Recognition Based on Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v33i01.33016818openalex-extracted
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
inferredcites →https://doi.org/10.1609/aaai.v35i15.17625openalex-extracted
DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition
inferredcites →https://doi.org/10.1609/aaai.v36i10.21348openalex-extracted
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer
inferredcites →https://doi.org/10.18653/v1/d15-1166openalex-extracted
Effective Approaches to Attention-based Neural Machine Translation
inferredcites →https://doi.org/10.18653/v1/d19-1015openalex-extracted
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
inferredcites →https://doi.org/10.18653/v1/n19-1423openalex-extracted
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
inferredcites →https://doi.org/10.21437/interspeech.2018-1477openalex-extracted
Exploring Spatio-Temporal Representations by Integrating Attention-based Bidirectional-LSTM-RNNs and FCNs for Speech Emotion Recognition
inferredcites →https://doi.org/10.21437/interspeech.2018-2261openalex-extracted
Self-Assessed Affect Recognition Using Fusion of Attentional BLSTM and Static Acoustic Features
inferredcites →https://doi.org/10.21437/interspeech.2019-1873openalex-extracted
wav2vec: Unsupervised Pre-Training for Speech Recognition
inferredcites →https://doi.org/10.21437/interspeech.2019-2561openalex-extracted
Data Augmentation Using GANs for Speech Emotion Recognition
inferredcites →https://doi.org/10.21437/interspeech.2019-2822openalex-extracted
Self-Attention for Speech Emotion Recognition
inferredcites →https://doi.org/10.21437/interspeech.2020-1653openalex-extracted
Multi-Modal Attention for Speech Emotion Recognition
inferredcites →https://doi.org/10.21437/interspeech.2020-1733openalex-extracted
Improving Speech Emotion Recognition Using Graph Attentive Bi-Directional Gated Recurrent Unit Network
inferredcites →https://doi.org/10.21437/interspeech.2020-2067openalex-extracted
Group Gated Fusion on Attention-Based Bidirectional Alignment for Multimodal Emotion Recognition
inferredcites →https://doi.org/10.21437/interspeech.2020-2572openalex-extracted
Adaptive Domain-Aware Representation Learning for Speech Emotion Recognition
inferredcites →https://doi.org/10.21437/interspeech.2020-3156openalex-extracted
A Multi-Scale Fusion Framework for Bimodal Speech Emotion Recognition
inferredcites →https://doi.org/10.21437/interspeech.2022-10230openalex-extracted
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition
inferredcites →https://doi.org/10.21437/interspeech.2022-74openalex-extracted
SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-extracted
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.4230/lipics.cp.2025.31openalex-extracted
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
inferredcites →https://doi.org/10.4230/oasics.nines.2026.13openalex-extracted
In-Kernel Aggregation and Broadcast Acceleration for Distributed Communication
inferredcites →https://doi.org/10.48550/arxiv.1409.0473openalex-extracted
Neural Machine Translation by Jointly Learning to Align and Translate
inferredcites →https://doi.org/10.48550/arxiv.1502.03044openalex-extracted
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
inferredcites →https://doi.org/10.48550/arxiv.1606.00061openalex-extracted
Hierarchical Question-Image Co-Attention for Visual Question Answering
inferredcites →https://doi.org/10.48550/arxiv.1807.03748openalex-extracted
Representation Learning with Contrastive Predictive Coding
inferredcites →https://doi.org/10.48550/arxiv.1911.05722openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2006.07733openalex-extracted
Bootstrap your own latent: A new approach to self-supervised Learning
inferredcites →https://doi.org/10.48550/arxiv.2012.08695openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2106.11532openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2109.04275openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2207.04697openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2211.07711openalex-extracted
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.