SERC-GCN: Speech Emotion Recognition In Conversation Using Graph Convolutional Networks
Bibliographic record
Abstract
Speech emotion recognition (SER) is the task of automatically recognizing emotions expressed in spoken language. Current approaches focus on analyzing isolated speech segments to identify a speaker’s emotional state. Meanwhile, recent text-based emotion recognition methods have effectively shifted towards emotion recognition in conversation (ERC) that considers conversational context. Motivated by this shift, here we propose SERC-GCN, a method for speech emotion recognition in conversation (SERC) that predicts a speaker’s emotional state by incorporating conversational context, speaker interactions, and temporal dependencies between utterances. SERC-GCN is a two-stage method. First, emotional features of utterance-level speech signals are extracted. Then, these features are used to form conversation graphs that are used to train a graph convolutional network to perform SERC. We empirically evaluate the effectiveness of SERC-GCN and show that it outperforms the current state-of-the-art methods on the IEMOCAP benchmark dataset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads agree on what is shown here.
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".