Audio Emotion Detection Application Utilizing AWS Cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Many applications today seek to understand user emotions through audio data, enhancing user engagement and experience. However, existing systems often lack the efficiency needed for real-time processing and accurate emotion detection. Developing a system that detects emotions from audio file uploads necessitates an infrastructure that can scale to meet user demands, ensure secure data processing, and integrate artificial intelligence services for emotion analysis. The key challenge is to design a cloud architecture that is scalable, secure, cost-efficient, and capable of analyzing emotions in audio files while delivering results promptly to users. Objective: To address the abovementioned challenges, this paper proposes a cloud-based system that allows users to upload audio files, analyses them to identify emotions (such as anger, calmness, disgust, fear, happiness, neutrality, sadness, surprise, etc.), and returns the detected emotions to the user in a timely manner. Methods: Two open datasets, Surrey Audio-Visual Expressed Emotion (SAVEE) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), were collected for training and testing the employed models using AWS cloud services. Results: The proposed method performed better than the existing methods for RAVDESS and SAVEE datasets by achieving a weighted accuracy of 93% and 94%, respectively, compared to four baselines, which obtained weighted accuracy of 71%, 73%, and 77%, respectively, for RAVDESS dataset, and 67% for SAVEE dataset. Conclusion: The system architecture has been crafted to be scalable and flexible, making it suitable for various applications, thus greatly enhancing user interactions.
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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 | 0.001 | 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) | 0.000 | 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 it