Multimodal Analysis for Speech Emotion Recognition
Bibliographic record
Abstract
Speech Emotion Recognition (SER) is becoming an essential field of study in today’s time due to its applications in customer service, enhancing human-computer interaction, and healthcare. However, SER is challenging due to variations in emotional expression across individuals, overlapping tones for different emotions, and limited datasets. The study focuses on recognizing emotions such as anger, happiness, sadness, fear, surprise, disgust, calmness, and neutral. We combine four popular datasets: CREMA-D (Crowdsourced Emotional Multimodal Actors Dataset), featuring speech from 91 actors; RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song), with recordings from 24 actors for multimodal approaches; SAVEE (Southern Alberta Vocal Expression Emotion), containing emotional speech from 4 male actors; and TESS (Toronto Emotional Speech Set), including speech samples from 2 female actors. The study implements three approaches: Convolutional Neural Networks (CNNs) for feature extraction, Long Short- Term Memory (LSTM) networks for recognizing temporal patterns, and Bidirectional LSTMs (Bi-LSTMs) with Multi head Attention for enhanced contextual understanding. Our proposed system achieves improved accuracy in SER, setting a foundation for its application in domains such as healthcare, customer service, and human-computer interaction.
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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) | 0.012 | 0.003 |
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".