## 4 Discussion  
The study of speech in patients with Alzheimer's disease using automatic speech processing is carried out primarily through face-to-face interviews, in which an interviewer asks questions, assigns speech tasks, or stimulates the subject to speak. Databases containing samples obtained from fully automated interview processes represent a small part of the total available. Despite the many advantages identified in automated sample collection, the number of systems currently implementing it remains limited. At present, this limitation could be one of the main barriers to demonstrating the usefulness of the results obtained by these automated methods. In this sense, exploring the potential of such tools and how they can be progressively improved are essential tasks in the field of automatic speech analysis for the detection of Alzheimer's disease. Figure 1: Graphical results of the feature selection process applied to the emotional temperature measures. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Interviewer** & \(te_{d}\) & \(\overline{te_{c}}\) \\ \hline Based on absence or & Automatic & 0.9 & 2.1 \\ presence of disease & Human & 0.55 & 1 \\ \hline Based on different grades & Automatic & 0.4 & 1.7 & 0.1 \\ of disease & Human & 0.45 & 0.95 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Relevance analysis of emotional temperature features (A: automatic interviewer; H: human interviewer). To gain a better understanding of the possible contribution of automatic interviewers applied to the discrimination of Alzheimer's disease through speech, several recordings obtained from both human and automatic interviewers were compared. Using the CSAP-19 database, as presented above, and considering the previously demonstrated usefulness of speech timing analysis, an emotional temperature feature extraction process was performed on the two types of speech present in the database. In this study, several univariate and multivariate analyses were carried out on the three populations included in the CSAP-19, as follows: healthy control subjects (HC), mild Alzheimer's (AD1), and moderate Alzheimer's (AD2). Following previous publications on several temporal measures of speech, we first analysed the five defined variables individually, as follows: discrete emotional temperature \((et_{d})\), average of continuous emotional temperature \((\overline{et_{c}})\), variance of continuous emotional temperature \((\sigma_{et_{c}}^{2})\), skewness of continuous emotional temperature \((\widetilde{\mu}_{et_{c}})\), and kurtosis of continuous emotional temperature \((Kurt_{et_{c}})\). From the results shown in Table 1, in Figure 2 the values obtained for each interviewer, grade, and defined variable are represented (human interviewer: dark grey; automatic interviewer: light grey). From the first analysis carried out, based on the analysis of the descriptive statistics, similar values and trends were obtained for the variables ETd \((et_{d})\), SKWETc \((\widetilde{\mu}_{et_{c}})\), and KRTETc \((Kurt_{et_{c}})\) independently of the interviewer used. On the other hand, for the five characteristics analysed, the HC subjects shared the same starting point regardless of the interviewer. Specifically, the variable ETd \((et_{d})\) for the human interviewer showed a drop in the mean values of emotional temperature in the AD2 patients compared to HC subjects. The KRTETc \((Kurt_{et_{c}})\) values less than three reflect less data concentrated around the average ET value. For AD2 patients, this fact is reflected to a greater degree for both interviewers. In general, regarding the rest of the variables, no clear trend or pattern was identified. In that sense, to detect it, another more exhaustive type of analysis is necessary. From the different nonparametric analyses carried out (Wilcoxon test, Kruskal-Wallis test, and median test), we were able to determine which of the five variables considered would be discriminant for Alzheimer's disease using four different comparisons among the populations.