The use of Automatic Speech Recognition in Education for Identifying Attitudes of the Speakers
Bibliographic record
Abstract
State-of-the-art Automatic Speech Recognition (ASR) systems convert the spoken words into a corresponding text. One of the problems faced in ASR is that speakers have a different way of pronouncing words, and their accents are different from one speaker to another due to age, gender, nationality, rapidity of words, expressive form of the speaker. This paper uses two data sets, Surrey Audio-Visual Expressed Emotion (SAVEE) and The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) data sets to determine the effect of the tone in the learning environment by using the ASR and check which classifier is giving the best result. Feature such energy, Mel filter Central coefficients, energy etc. were extracted using jAudio and Waikato Environment for Knowledge Analysis (WEKA) data mining tools was used for classification. Classifiers called multilayer Perceptron (MLP) neural network model, Support Vector Machines (SVM), Simple Logistic Regression (SLR), K-Nearest Neighbour (K-NN) and Random Forests (RF) was used to obtain the results of the emotion state for the both data sets. The data sets used to train the classifiers are in ARFF format. The results show that SAVEE data sets overcomes RAVDESS data sets in overall emotion classification performance. The result shows that RF performed better than the other classifier. The performance of classification models is evaluated in WEKA using 10-fold cross validation. The presented study examines seven emotions- anger, happiness, sadness, fear, surprise, disgust and neutral.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".