From: Automatic detection of attachment style in married couples through conversation analysis
Low-level acoustic feature fusion | Low-level acoustic and I-vector feature fusion | Low-level acoustic and I-vector score fusion | |||||||||||||
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Feature type | Accuracy | Precision | Recall | F1 score | Feature type | Accuracy | Precision | Recall | F1 score | Feature type | Accuracy | Precision | Recall | F1 score | |
SVM | MIM (30) | 0.73 | 0.72 | 0.72 | 0.72 | MIM (30) | 0.73 | 0.72 | 0.72 | 0.72 | MIM (10) | 0.61 | 0.62 | 0.52 | 0.57 |
mRMR (15) | 0.68 | 0.69 | 0.65 | 0.67 | mRMR (15) | 0.69 | 0.69 | 0.66 | 0.67 | mRMR (30) | 0.57 | 0.56 | 0.53 | 0.55 | |
JMI (15) | 0.67 | 0.65 | 0.7 | 0.68 | JMI (15) | 0.67 | 0.65 | 0.71 | 0.68 | JMI (5) | 0.65 | 0.65 | 0.62 | 0.64 | |
Decision tree | MIM (5) | 0.75 | 0.74 | 0.74 | 0.76 | MIM (50) | 0.72 | 0.7 | 0.74 | 0.72 | MIM (30) | 0.75 | 0.75 | 0.74 | 0.75 |
mRMR (30) | 0.74 | 0.77 | 0.69 | 0.73 | mRMR (30) | 0.79 | 0.79 | 0.77 | 0.78 | mRMR (10) | 0.64 | 0.63 | 0.67 | 0.65 | |
JMI (30) | 0.71 | 0.7 | 0.72 | 0.71 | JMI (10) | 0.73 | 0.73 | 0.71 | 0.72 | JMI (10) | 0.74 | 0.72 | 0.77 | 0.75 | |
Random forest | MIM (30) | 0.8 | 0.81 | 0.76 | 0.78 | MIM (30) | 0.8 | 0.81 | 0.76 | 0.78 | MIM (30) | 0.76 | 0.75 | 0.77 | 0.76 |
mRMR (15) | 0.8 | 0.84 | 0.72 | 0.78 | mRMR (15) | 0.8 | 0.81 | 0.76 | 0.78 | mRMR (30) | 0.79 | 0.76 | 0.83 | 0.79 | |
JMI (15) | 0.78 | 0.8 | 0.74 | 0.77 | JMI (10) | 0.77 | 0.76 | 0.77 | 0.77 | JMI (30) | 0.77 | 0.78 | 0.74 | 0.76 | |
AdaBoost | MIM (30) | 0.85 | 0.87 | 0.81 | 0.84 | MIM (30) | 0.84 | 0.84 | 0.83 | 0.83 | MIM (50) | 0.8 | 0.81 | 0.79 | 0.8 |
mRMR (30) | 0.82 | 0.84 | 0.79 | 0.81 | mRMR (30) | 0.82 | 0.82 | 0.81 | 0.82 | mRMR (30) | 0.79 | 0.77 | 0.81 | 0.79 | |
JMI (50) | 0.8 | 0.83 | 0.76 | 0.79 | JMI (30) | 0.8 | 0.8 | 0.8 | 0.8 | JMI (15) | 0.81 | 0.8 | 0.83 | 0.81 | |
Gradient boosting | MIM (30) | 0.81 | 0.83 | 0.77 | 0.8 | MIM (30) | 0.84 | 0.84 | 0.83 | 0.83 | MIM (30) | 0.78 | 0.77 | 0.79 | 0.78 |
mRMR (50) | 0.81 | 0.82 | 0.79 | 0.81 | mRMR (50) | 0.83 | 0.85 | 0.79 | 0.82 | mRMR (30) | 0.8 | 0.81 | 0.79 | 0.8 | |
JMI (30) | 0.82 | 0.82 | 0.81 | 0.82 | JMI (30) | 0.82 | 0.81 | 0.83 | 0.82 | JMI (15) | 0.8 | 0.8 | 0.81 | 0.8 | |
Extra tree | MIM (30) | 0.84 | 0.85 | 0.81 | 0.83 | MIM (30) | 0.84 | 0.84 | 0.83 | 0.83 | MIM (30) | 0.8 | 0.8 | 0.77 | 0.79 |
mRMR (15) | 0.81 | 0.82 | 0.79 | 0.8 | mRMR (15) | 0.81 | 0.82 | 0.79 | 0.81 | mRMR (30) | 0.78 | 0.78 | 0.76 | 0.77 | |
JMI (15) | 0.78 | 0.81 | 0.74 | 0.77 | JMI (15) | 0.79 | 0.82 | 0.74 | 0.77 | JMI (30) | 0.77 | 0.79 | 0.72 | 0.76 | |
XGBoost | MIM (30) | 0.82 | 0.82 | 0.81 | 0.82 | MIM (30) | 0.82 | 0.82 | 0.81 | 0.82 | MIM (30) | 0.79 | 0.77 | 0.81 | 0.79 |
mRMR (30) | 0.79 | 0.78 | 0.79 | 0.79 | mRMR (30) | 0.79 | 0.79 | 0.79 | 0.78 | mRMR (30) | 0.79 | 0.78 | 0.79 | 0.79 | |
JMI (15) | 0.77 | 0.75 | 0.79 | 0.77 | JMI (15) | 0.77 | 0.77 | 0.76 | 0.76 | JMI (30) | 0.75 | 0.75 | 0.73 | 0.74 | |
Artificial neural network | MIM(15) | 0.7 | 0.71 | 0.63 | 0.67 | MIM(15) | 0.69 | 0.71 | 0.64 | 0.67 | MIM(50) | 0.73 | 0.73 | 0.71 | 0.72 |
mRMR(15) | 0.7 | 0.7 | 0.65 | 0.68 | mRMR(15) | 0.69 | 0.7 | 0.65 | 0.68 | mRMR(50) | 0.68 | 0.69 | 0.62 | 0.65 | |
JMI(15) | 0.63 | 0.63 | 0.62 | 0.62 | JMI(15) | 0.63 | 0.63 | 0.62 | 0.63 | JMI(50) | 0.77 | 0.78 | 0.74 | 0.76 | |
SD-DNN | - | 0.85 | 0.97 | 0.72 | 0.83 | - | - | - | - | - | - | - | - | - | - |