A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions
Bibliographic record
Abstract
Children who fall into the autism spectrum have difficulty communicating with others. In this work, a speech emotion recognition model has been developed to help children with Autism Spectrum Disorder (ASD) identify emotions in social interactions. The model is created using the Python programming language to develop a machine learning model based on the Support Vector Machine (SVM). SVM has proven to yield high accuracies when classifying inputs in speech processing. Individual audio databases are specifically designed to train models for the emotion recognition task. One such speech corpus is the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), which is used to train the model in this work. Acoustic feature extraction will be part of the pre-processing step utilizing Python libraries. The libROSA library is used in this work. The first 26 Mel-frequency Cepstral Coefficients (MFCCs) and the zero-crossing rate (ZCR) are extracted and used as the acoustic features to train the machine learning model. The final SVM model provided a test accuracy of 77%. This model also performed well when significant background noise was introduced to the RAVDESS audio recordings, for which it yielded a test accuracy of 64%.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".