## 4 Discussion
(**c**) Sensitivity of the different classifiers: human and automatic interviewers; classification by AD presence or absence. (**d**) Sensitivity of the different classifiers: human and automatic interviewers; classification by AD grades. (**e**) Specificity for the different classifiers: human and automatic interviewers; classification by AD presence or absence. (**f**) Specificity for the different classifiers: human and automatic interviewers; classification by AD grades. sensitivity of 72% for the logistic and KNN classifier, n = 3) and automatic (with a sensitivity of 73.9% for the KNN classifier with n = 5). Nevertheless, it is crucial to point out that the sensitivity values obtained, regardless of the classifier used, were low. Specifically, for the case where sensitivity peaks with the automatic interviewer, we found that specificity was remarkably low (26.1%). Although our main interest lies in sensitivity, which is the ability to detect subjects with pathology, a low specificity indicates an unacceptable excess of false positives in the classification. The accuracy reflects the dispersion of sample values and refers to the ratio of correct predictions to total predictions. Conceptually, we must consider the \"cost\" associated with each type of classification error in the algorithm when deciding which parameter to prioritise. Specifically, for our study, the detection of pathological subjects is crucial, so sensitivity should be the main parameter for evaluating our classifiers. However, as discussed, it is also necessary to find a balance between sensitivity and specificity to achieve a truly balanced system. Overall, the best-performing classifiers were KNN (for n = 3 and n = 5) and the logistic classifier. In terms of sensitivity values, both interviewers achieved their best results when classifying by grade, with values around 70%. When analysing the results, from the point of view of the interviewer, neither of the two interviewers stands out from the other in any of the parameters evaluated. The last multivariate analysis carried out in this study was the MANOVA variance test. The data obtained and presented in Table 7 indicate that, in all statistical aspects evaluated, the set of emotional temperature features is discriminant with the HC and AD populations and in the case of employing the automatic interviewer. However, when considering the grouping variables AD1 and AD2, the statistic values for both interviewers are well above the upper limit set for the \(p\)-value (_Prob \(|z|=0.05\)_). This phenomenon might be in line with the results of the nonparametric tests performed earlier. In these tests, the HC-AD discrimination showed a higher number of variables that met the limit established for the \(p\)-value. Specifically, in the three univariate nonparametric analyses carried out, none of the five variables independently proved to be discriminatory when comparing the AD1 and AD2 populations. This situation can again be attributed to the number of samples available for each population (the more samples, the more conclusive the results), as well as the additional difficulty in differentiating between two pathological voices of different grades. As can be deduced from the results obtained, the samples that would reject the null hypothesis in terms of AD discrimination are those generated by induced speech, i.e., those obtained through the automatic interviewer. In general terms, in this study, we have evaluated the discrimination capacity between a set of samples obtained using an automatic interviewer and another set obtained using a human interviewer. All samples have been classified based on the emotional temperature measures extracted from them. As can be inferred from the data presented, the samples show a similar pattern in their measures regardless of the interviewer used, being able to discriminate AD from induced or spontaneous speech.