Table 4 Results of machine learning classifiers using MFCC features.
Model | Class | Precision | Recall | F1 Score | Model | Class | Precision | Recall | F1 Score |
|---|---|---|---|---|---|---|---|---|---|
DT | 0 | 0.96 | 0.92 | 0.94 | LR | 0 | 1.0 | 0.92 | 0.96 |
1 | 1.00 | 1.00 | 1.00 | 1 | 1.00 | 1.00 | 1.00 | ||
2 | 0.92 | 0.96 | 0.94 | 2 | 0.92 | 1.0 | 0.96 | ||
Micro avg. | 0.96 | 0.96 | 0.96 | Micro avg. | 0.97 | 0.97 | 0.97 | ||
Weighted avg. | 0.96 | 0.96 | 0.96 | Weighted avg. | 0.97 | 0.97 | 0.97 | ||
Accuracy | 0.96 | Accuracy | 0.97 | ||||||
SVC | 0 | 0.98 | 0.98 | 0.98 | NB | 0 | 0.63 | 0.81 | 0.71 |
1 | 1.00 | 1.00 | 1.00 | 1 | 1.00 | 1.00 | 1.00 | ||
2 | 0.98 | 0.98 | 0.98 | 2 | 0.74 | 0.53 | 0.62 | ||
Micro avg. | 0.99 | 0.99 | 0.99 | Micro avg. | 0.79 | 0.78 | 0.78 | ||
Weighted avg. | 0.99 | 0.99 | 0.99 | Weighted avg. | 0.79 | 0.78 | 0.77 | ||
Accuracy | 0.99 | Accuracy | 0.78 | ||||||
KNN | 0 | 0.78 | 0.79 | 0.78 | RF | 0 | 0.96 | 0.94 | 0.95 |
1 | 1.00 | 1.00 | 1.00 | 1 | 1.00 | 1.00 | 1.00 | ||
2 | 0.79 | 0.78 | 0.78 | 2 | 0.94 | 0.96 | 0.95 | ||
Micro avg. | 0.86 | 0.86 | 0.86 | Micro avg. | 0.97 | 0.97 | 0.97 | ||
Weighted avg. | 0.85 | 0.85 | 0.85 | Weighted avg. | 0.97 | 0.97 | 0.97 | ||
Accuracy | 0.85 | Accuracy | 0.97 | ||||||
HardVoting | 0 | 0.98 | 0.96 | 0.97 | Soft Voting | 0 | 0.98 | 0.96 | 0.97 |
1 | 1.00 | 1.00 | 1.00 | 1 | 1.00 | 1.00 | 1.00 | ||
2 | 0.96 | 0.98 | 0.97 | 2 | 0.96 | 0.98 | 0.97 | ||
Micro avg. | 0.98 | 0.98 | 0.98 | Micro avg. | 0.98 | 0.98 | 0.98 | ||
Weighted avg. | 0.98 | 0.98 | 0.98 | Weighted avg. | 0.98 | 0.98 | 0.98 | ||
Accuracy | 0.98 | Accuracy | 0.98 | ||||||
