A Novel Emotion-Aware Method Based on the Fusion of Textual Description of Speech, Body Movements, and Facial Expressions
Bibliographic record
Abstract
Emotion computing is a necessary part of advanced human–computer interaction. An appropriate description of a character’s facial expressions, body languages, and speaking styles in novels always enables readers to infer the character’s emotions. Moreover, multimodal information is complementary and integrated. Fusing the information from multiple modes into a textual modal can get better fusion results and overcome the bias of understanding the unimodal information. Inspired by these facts, we develop a novel emotion-aware method by the fusion of textual description of speech, body movements, and facial expression, which reduces the dimensionality of speech, body movements, and facial expressions by unifying three types of information into a unified component. Specifically, to fuse multimodel features for emotion recognition, we propose a two-stage neural network. First, bidirectional long short-term memory-conditional random fields (Bi-LSTM-CRF) and back-propagation neural network (BPNN) are used to analyze the extracted vocal and visual features of facial expressions, body movements, and speeches, which aims to obtain textual descriptions of different features. Second, the textual descriptions of the features are fused through a neural network with a self-organization map (SOM) layer and are used to compensate layers that are trained by web-based corpus. The advantages of this method are to utilize depth information to track facial and bodily movement, and employ an explainable textual intermediate representation to fuse the features. We experimentally tested the emotion-aware system in real-world applications, and the results indicate that our system can quickly and steadily recognize human emotions. Compared with other unimodal and multimodal-fusion algorithms, our method is more precise, which can improve the accuracy by up to 30% compared with the unimodal method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".