Table 5.
Comparisons of the performance of machine learning classifiers for Mel-frequency cepstral coefficients (MFCCs), extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and Interspeech Computational Paralinguistics Challenge (COMPARE) feature sets of passage task (N=318).
| Feature and classifier | Test set | ||||
|
|
ACCa (%), mean (SD) | PRECb (%), mean (SD) | RECc (%), mean (SD) | F1-scored (%), mean (SD) | |
| MFCCs | |||||
|
|
SVMe | 70.73 (5.93) | 70.00 (6.29) | 68.18 (6.19) | 68.63 (6.63) |
|
|
LDAf | 69.95 (8.38) | 70.57 (8.23) | 69.79 (8.34) | 69.49 (8.60) |
|
|
kNNg | 63.45 (7.76) | 63.55 (8.05) | 63.25 (7.91) | 63.13 (7.87) |
|
|
RFh | 68.54 (7.75) | 66.45 (7.40) | 67.98 (8.09) | 67.27 (8.74) |
| eGeMAPS | |||||
|
|
SVM | 59.38 (2.08) | 59.35 (2.15) | 58.89 (2.15) | 58.58 (2.34) |
|
|
LDA | 58.14 (2.99) | 57.96 (3.34) | 57.21 (2.96) | 56.62 (2.99) |
|
|
kNN | 61.88 (4.37) | 62.11 (4.85) | 64.05 (4.71) | 60.66 (4.37) |
|
|
RF | 57.81 (2.21) | 57.33 (2.91) | 56.74 (2.17) | 56.10 (2.36) |
| COMPARE | |||||
|
|
SVM | 63.44 (2.44) | 63.44 (2.53) | 62.96 (2.58) | 62.80 (2.71) |
|
|
LDA | 62.19 (4.93) | 62.17 (4.94) | 62.10 (4.87) | 62.03 (4.90) |
|
|
kNN | 65.63 (3.13) | 65.61 (3.19) | 65.49 (3.07) | 65.44 (3.06) |
|
|
RF | 68.75 (1.40) | 69.69 (1.27) | 67.84 (1.49) | 67.60 (1.63) |
aACC: accuracy.
bPREC: precision.
cREC: recall.
dF1-score: the weighted average of precision and recall.
eSVM: support vector machine.
fLDA: linear discriminate analysis.
gkNN: k-nearest neighbor.
hRF: random forest.