ArticleFrontiers in neurorobotics2023
Multimodal transformer augmented fusion for speech emotion recognition.
Article in Frontiers in neurorobotics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- MEPT-LLM: a multimodal generative AI model for identifying and understanding cultural-emotional barriers in the language classroom.Frontiers in psychology · 2026Article
- A gender-emotion interaction multi-task network for depression recognition via transformer-based multimodal fusion.Frontiers in psychiatry · 2026Article
- FedSER-XAI: PSO-optimized multi-stream cross-attention transformer with graph features for explainable federated speech emotion recognition.Scientific reports · 2025Article
- Bridging language gaps: The role of NLP and speech recognition in oral english instruction.MethodsX · 2025Article
- MemoCMT: multimodal emotion recognition using cross-modal transformer-based feature fusion.Scientific reports · 2025Article
- Potential of Large Language Models in Health Care: Delphi Study.Journal of medical Internet research · 2024Article
- Speech emotion recognition via graph-based representations.Scientific reports · 2024Article
- Evaluation and analysis of visual perception using attention-enhanced computation in multimedia affective computing.Frontiers in neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Speech emotion recognition is challenging due to the subjectivity and ambiguity of emotion. In recent years, multimodal methods for speech emotion recognition have achieved promising results. However, due to the heterogeneity of data from different modalities, effectively integrating different modal information remains a difficulty and breakthrough point of the research. Moreover, in view of the limitations of feature-level fusion and decision-level fusion methods, capturing fine-grained modal interactions has often been neglected in previous studies. We propose a method named multimodal transformer augmented fusion that uses a hybrid fusion strategy, combing feature-level fusion and model-level fusion methods, to perform fine-grained information interaction within and between modalities. A Model-fusion module composed of three Cross-Transformer Encoders is proposed to generate multimodal emotional representation for modal guidance and information fusion. Specifically, the multimodal features obtained by feature-level fusion and text features are used to enhance speech features. Our proposed method outperforms existing state-of-the-art approaches on the IEMOCAP and MELD dataset.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.