Table 4.
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 digit task (N=318).
| Feature and classifier | Test set | ||||||||
|
|
ACCa (%), mean (SD) | PRECb (%), mean (SD) | RECc (%), mean (SD) | F1-scored (%), mean (SD) | |||||
| MFCCs | |||||||||
|
|
SVMe | 53.75 (4.80) | 53.82 (4.96) | 53.76 (4.87) | 53.62 (4.74) | ||||
|
|
LDAf | 56.25 (5.76) | 56.07 (5.84) | 55.92 (5.66) | 55.87 (5.64) | ||||
|
|
kNNg | 51.25 (5.80) | 50.80 (4.41) | 50.82 (4.36) | 50.61 (4.44) | ||||
|
|
RFh | 52.81 (3.26) | 51.66 (4.09) | 51.63 (3.48) | 50.24 (4.24) | ||||
| eGeMAPS | |||||||||
|
|
SVM | 51.84 (4.62) | 51.32 (4.58) | 51.30 (4.56) | 51.17 (4.60) | ||||
|
|
LDA | 48.01 (2.50) | 47.95 (2.69) | 48.04 (2.64) | 47.92 (2.69) | ||||
|
|
kNN | 45.36 (3.42) | 45.60 (3.48) | 45.52 (3.39) | 45.13 (3.30) | ||||
|
|
RF | 56.21 (3.56) | 56.15 (4.89) | 55.10 (3.68) | 52.96 (4.25) | ||||
| COMPARE | |||||||||
|
|
SVM | 57.81 (5.09) | 57.64 (5.69) | 57.08 (5.78) | 55.73 (6.77) | ||||
|
|
LDA | 62.19 (4.30) | 62.32 (4.49) | 62.25 (4.41) | 62.09 (4.31) | ||||
|
|
kNN | 45.31 (4.01) | 45.14 (4.03) | 45.16 (4.03) | 45.10 (4.03) | ||||
|
|
RF | 73.44 (1.56) | 73.84 (1.79) | 72.96 (1.49) | 72.99 (1.53) | ||||
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.