Multilayer Neural Network Based Speech Emotion Recognition for燬mart燗ssistance
Bibliographic record
Abstract
Day by day, biometric-based systems play a vital role in our daily lives. This paper proposed an intelligent assistant intended to identify emotions via voice message. A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions. This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes (LED) alert signals and it also keep track of the places like households, hospitals and remote areas, etc. The proposed approach is able to detect seven emotions: worry, surprise, neutral, sadness, happiness, hate and love. The key elements for the implementation of speech emotion recognition are voice processing, and once the emotion is recognized, the machine interface automatically detects the actions by buzzer and LED. The proposed system is trained and tested on various benchmark datasets, i.e., Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) database, Acoustic-Phonetic Continuous Speech Corpus (TIMIT) database, Emotional Speech database (Emo-DB) database and evaluated based on various parameters, i.e., accuracy, error rate, and time. While comparing with existing technologies, the proposed algorithm gave a better error rate and less time. Error rate and time is decreased by 19.79%, 5.13 s. for the RAVDEES dataset, 15.77%, 0.01 s for the Emo-DB dataset and 14.88%, 3.62 for the TIMIT database. The proposed model shows better accuracy of 81.02% for the RAVDEES dataset, 84.23% for the TIMIT dataset and 85.12% for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM) and Support Vector Machine (SVM) Model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".