Table 3.
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 vowel task (N=318).
| Feature and classifier | Test set | ||||
|
|
ACCa (%), mean (SD) | PRECb (%), mean (SD) | RECc (%), mean (SD) | F1-scored (%), mean (SD) | |
| MFCCs | |||||
|
|
SVMe | 56.56 (3.81) | 56.22 (4.01) | 55.82 (3.84) | 55.43 (3.91) |
|
|
LDAf | 54.69 (2.88) | 54.25 (2.99) | 53.98 (2.80) | 53.65 (2.75) |
|
|
kNNg | 57.19 (5.60) | 56.84 (5.85) | 56.65 (5.58) | 56.47 (5.80) |
|
|
RFh | 60.63 (1.53) | 61.24 (2.07) | 59.41 (1.44) | 58.40 (1.31) |
| eGeMAPS | |||||
|
|
SVM | 59.38 (5.93) | 59.27 (6.37) | 58.59 (6.16) | 57.90 (6.58) |
|
|
LDA | 59.69 (2.60) | 59.44 (2.66) | 59.16 (2.66) | 59.05 (2.71) |
|
|
kNN | 59.37 (3.13) | 59.30 (3.17) | 59.29 (3.18) | 59.23 (3.18) |
|
|
RF | 61.25 (2.86) | 61.67 (3.20) | 60.16 (2.99) | 59.32 (3.39) |
| COMPARE | |||||
|
|
SVM | 48.75 (3.48) | 48.57 (3.62) | 48.67 (3.44) | 48.26 (3.70) |
|
|
LDA | 51.25 (3.19) | 51.37 (3.46) | 51.37 (3.40) | 51.07 (3.38) |
|
|
kNN | 59.38 (5.23) | 59.12 (5.42) | 58.78 (5.32) | 58.61 (5.40) |
|
|
RF | 72.80 (2.44) | 73.70 (2.19) | 72.14 (2.64) | 72.04 (2.77) |
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.