Sistema integrado de reconocimiento de emociones para interacción hombre-máquina
Bibliographic record
Abstract
Resumen: El principal objetivo de esta investigación es desarrollar un sistema integrado de reconocimiento de emociones para robots de servicios en tareas domésticas. Para construir y entrenar el modelo se utilizan la red convolucional densa DenseNet y el conjunto de datos audiovisuales Ryerson sobre lenguaje y canción emocional RAVDESS. El sistema está compuesto por dos lazos de reconocimiento de emociones que estiman el estado desde una perspectiva multimodal. Un primer lazo utiliza el rostro para establecer el estado emocional a partir de las facciones, mientras que el segundo utiliza la voz. Los resultados de las pruebas en laboratorio muestran un alto desempeño del sistema gracias a un modelo que aporta información cuando el otro es incapaz. Dicha interacción permite pensar en la integración de módulos adicionales para incrementar la confiabilidad del robot. En conclusión, la arquitectura en paralelo incrementa considerablemente la capacidad del sistema de reconocimiento de emociones.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.014 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".