## 3 Results  
### Confrontational Naming Analysis  
Data obtained through the proposed system for naming, repetition, and comprehension tasks from non-aphasic, Anomic, and [PERSON]'s participants were analyzed to determine distinguishable characteristics of each sample. Figure 4 depicts the confrontation naming performance of three samples. The average time taken to elicit each word by each sample is illustrated in Figure 4a. Considering the total word list, on average non-aphasic participants took 2.51 seconds to elicit a target word. Contrastingly, the aphasic sample ([PERSON] and [PERSON]'s) took 10.73 seconds on average to elicit a target word. The system obtained the first four formant frequencies (F\({}_{1}\)-F\({}_{4}\)) of the entire confrontation naming task (n = 30 words) for each participant. Upon completing the total word list, averages for each formant frequency were obtained for each participant.  
Consequently, derived averages were separately analyzed considering the clinical diagnosis, in order to obtain average formant frequency values for non-aphasic, Anomic, and [PERSON]'s samples. Accordingly, formant frequency variation for the aforementioned samples are presented in Figure 4b, which depicts a higher formant frequency average for Anomic aphasia and lower formant frequency average for [PERSON]'s aphasia than the non-aphasic sample. Further, paired \(t\)-test confirmed that formant frequencies of [PERSON] [\(t\) (17) = 1.9977, (\(P\) value \(<\) 0.05)] and [PERSON]'s [\(t\) (11) = 2.5642,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Age** & **Gender** & **Aphasia Type** \\ \hline AH001 & 62 & Female & [PERSON]’s \\ AH002 & 41 & Male & Anomic \\ AH003 & 52 & Male & Anomic \\ AH004 & 53 & Female & Anomic \\ AH005 & 28 & Male & Anomic \\ AH006 & 59 & Female & [PERSON]’s \\ AH007 & 67 & Female & Anomic \\ AH008 & 49 & Male & Anomic \\ AH009 & 71 & Female & [PERSON]’s \\ AH010 & 38 & Male & [PERSON]’s \\ AH011 & 63 & Male & Anomic \\ AH012 & 55 & Female & [PERSON]’s \\ AH013 & 57 & Female & Anomic \\ AH014 & 66 & Male & Anomic \\ AH015 & 47 & Male & [PERSON]’s \\ AH016 & 43 & Female & Anomic \\ AH017 & 72 & Male & [PERSON] \\ AH018 & 66 & Male & [PERSON]’s \\ AH019 & 51 & Male & [PERSON] \\ AH020 & 44 & Male & [PERSON]’s \\ AH021 & 46 & Female & [PERSON] \\ AH022 & 68 & Female & [PERSON]’s \\ AH023 & 52 & Male & [PERSON] \\ AH024 & 40 & Female & [PERSON] \\ AH025 & 69 & Male & [PERSON]’s \\ AH026 & 50 & Male & [PERSON] \\ AH027 & 63 & Female & [PERSON]’s \\ AH028 & 48 & Male & [PERSON] \\ AH029 & 39 & Female & [PERSON] \\ AH030 & 62 & Male & [PERSON]’s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participants’ demographic data and initial clinical diagnosis.  
(\(P\) value \(<0.05\))] are significantly different from the non-aphasic sample. Figure 4c presents the average number of pathological speech characteristics encountered in the [PERSON] and [PERSON]'s samples, since the control sample was free from these characteristics. Comparatively, [PERSON] participants had 15% fewer pathological characteristics than [PERSON]'s participants. Upon the completion of the confrontation naming task, the mathematical module calculates \(Sp_{NA}\) for each participant. The algorithm uses the naming score to perform a differential diagnosis of [PERSON] and [PERSON]'s aphasia. The [PERSON] sample's mean \(Sp_{NA}\) value was always higher than mean \(Sp_{NA}\) of the [PERSON]'s sample. Thus, the proposed algorithm can accurately distinguish the naming performance of patients with [PERSON] and [PERSON]'s aphasia. A summarized analysis of confrontation naming performance is given below in Table 2.