## 3 Results  
Table 2 shows the 95% confidence intervals of the mean values for the selected features, categorized by groups. Out of the ninety-two input features analyzed, only seven features meet the established criteria using the offline evaluation data. In previous studies, using different criteria, 21 features were selected in [24] and 27 features were selected in [9]. Regarding the selected features, jitterLocal_sma3 nz_stddevNorm represents the coefficient of variation of deviations in consecutive F0 period lengths. In the energy domain, loudness_sma3_percentile20.0 indicates the 20 th percentile estimate of perceived signal intensity from an auditory spectrum. Finally, in the temporal domain, loudnessPeaksPerSec refers to the number of loudness peaks per second, StddevVoicedSegmentLengthSec represents the standard deviation of continuously voiced regions, silencePercentage indicates the duration percentage of unvoiced regions, silencesPerSecond represents the number of silences per second, and silencesMean denotes the mean length of unvoiced regions. The confidence interval values in Table 2 reveal that a less stable F0 contour (higher f1 feature) is associated with an abnormal utterance pronunciation at a perceptual level. A weaker intensity (lower e1 feature) is penalized more. Utterances belonging to wrong groups exhibit slower speech (lower d1 feature), more speed changes (higher d2 feature), increased frequency of inner pauses (higher d3 and d4 features), and longer pauses (higher d5 feature). Furthermore, Table 2 allows a comparison of feature values between typical and DS speakers. It is relevant to note that there is a separation in the intervals between typical and right productions for all features, except for d2, where the confidence intervals overlap between right and typical utterances. The proposed feature selection procedure successfully pinpointed the seven most informative variables out of the ninety-two analyzed ones, enabling the prediction of prosodic quality with satisfactory outcomes. Three other established feature selection methods have also been used for comparison. As shown in Table 3, the feature set obtained with the proposed method, comprising seven features, yields superior classification accuracy compared to using all ninety-two features across the three examined classification methods. This indicates that these seven features encapsulate essential aspects for effectively classifying samples. Compared to other feature sets of equal size (seven features) acquired through conventional feature \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Typical** & **DS Right** & **DS Wrong** \\  & **Speakers** & **Productions** & **Productions** \\ \hline F0 domain & & & \\ \hline f1 jitterLocal\_sma3 nz\_stddevNorm & (1.15, 1.18) & (1.32, 1.43) & (1.52, 1.66) \\ \hline Energy domain & & & \\ \hline e1 loudness\_sma3\_percentile20.0 & (0.85, 0.89) & (0.71, 0.78) & (0.63, 0.71) \\ \hline Temporal domain & & & \\ \hline d1 loudnessPeaksPerSec & (5.74, 5.84) & (4.10, 4.32) & (3.77, 4.06) \\ d2 StddevVoicedSegmentLengthSec & (0.18, 0.19) & (0.18, 0.23) & (0.25, 0.33) \\ d3 silencePercentage & (0.11, 0.13) & (0.08, 0.11) & (0.21, 0.27) \\ d4 silencesPerSecond & (0.37, 0.41) & (0.27, 0.35) & (0.52, 0.63) \\ d5 silencesMean & (0.20, 0.22) & (0.13, 0.18) & (0.31, 0.41) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of automatically chosen frequency, energy, and temporal features. All of these features exhibit statistically significant differences (as determined by the Mann–Whitney test with a \(p\)-value less than 0.01) when comparing correct and incorrect productions of people with Down syndrome. Moreover, in the selected features, the difference between the mean of productions of TD speakers and the mean of right productions is lower than the difference between the mean of productions of TD speakers and the mean of wrong productions. The interpretation of these features is detailed in [9].