Analysing the Impact of LSTM and MFCC on Speech Emotion Recognition Accuracy
Bibliographic record
Abstract
Identifying emotional states through the analysis of vocalisations is a difficult task in the realm of human-computer interaction (HCI). Many research methodologies have been employed, and more recent research has advocated the use of deep learning algorithms as viable substitutes for the methodologies that are now employed in SER. The Toronto Emotional Speech Set (TESS) dataset is used in this research work to present a speech emotion detection system based on the Long Short-Term Memory (LSTM) model and Mel Frequency Cepstral Coefficients (MFCC) classification. With the use of voice cues, the suggested system intends to appropriately categorise emotions including anger, disgust, fear, happiness, neutral, and sadness. The LSTM model is trained to categorise the emotions, and the MFCC approach is utilised to extract features from the speech signals. The recognition rate of the trained network was then tested using a set of labelled emotion speech samples. The number of neurons and layers are modified for optimisation based on the MFCC accuracy rate. Outside the preview of earlier studies, this study places more focus on speech-based outcomes than on text-based ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".