Mel-Spectrograms Based LSTM Model for Speech Emotion Recognition
Bibliographic record
Abstract
Emotion recognition from audio data holds immense potential in revolutionizing humancomputer interaction (HMI), affective computing, and psychological health monitoring.This paper delves into a novel deep learning approach that leverages the strengths of multimodal features mined from audio signals.We propose a model that transcends the disadvantages of existing methods by combining Mel-Frequency Cepstral Coefficients (MFCCs) with high-level representations extracted from a pre-trained DenseNet architecture.MFCCs provide a compressed representation of the audio signal's spectral characteristics, capturing crucial emotional cues like pitch and intensity.These learned patterns can translate to the domain of audio emotion recognition, enabling the model to identify subtle emotional nuances that might be difficult to capture with traditional feature engineering techniques.Our deep learning model, comprised of dense layers, fosters robust performance in accurately classifying emotions across diverse categories.We used a Melspectrograms-based LSTM model for speech emotion recognition that effectively identifies various emotions.We rigorously evaluate the proposed approach on the TESS dataset.The experimental results are truly compelling, showcasing a staggering accuracy of 100%.This exceptional performance signifies the effectiveness of the multimodal approach in extracting and interpreting emotional cues from audio data.
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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 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".