Emotion Recognition Using Speech
Bibliographic record
Abstract
Research on emotion recognition from speech has become crucial in the field of human-computer interaction, with potential uses in industries like entertainment, healthcare, and customer service. The goal of this system is to employ machine learning techniques to create a reliable system that can recognise human emotions from vocal expressions. Mel Frequency Cepstral Coefficients are sound characteristics that are extracted from speech signals by the system. Convolutional Neural Networks, Long Short-Term Memory networks, and Gated Recurrent Units are among the deep learning methods it uses to efficiently categorise emotions. Two popular datasets are used to optimise the model: the SAVEE (Surrey Audio-Visual Expressed Emotion) and RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) datasets provide a large and varied collection of emotional speech samples. The suggested approach, which has shown excellent accuracy in real-world assessment contexts, seeks to differentiate eight different emotional characteristics: anger, fear, repulsion, joy, sorrow, surprise, neutrality, and calm. This system's ability to identify different emotional states can greatly improve user experience and interaction in a number of real-world applications, such as voice assistants, sentiment analysis, and mental health monitoring. The results show how well the selected approaches discern between different emotions and highlight how speech emotion detection systems might be used in commonplace technology.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".