## 4 Discussion  
Experts became interested in automating speech and language assessments owing to the technological boost in ASR. In general, ASR performance is tightly coupled with the quality of the input speech signal [23]. Hence, applicability of ASR in pathological speech signal evaluation has become controversial despite its benefits. Nevertheless, ASR free features of speech signals are considered as promising alternatives when evaluating pathological speech signals [16]. Although a lot of insightful works have been performed in the autonomous language skills evaluation domain [9; 11; 12; 13; 23; 24; 25], there is still a lot of room left for research. Hence, herein we incorporated ASR free features of pathological speech characteristics to develop a quantitative and objective aphasia assessment tool, which differentially diagnoses [PERSON] and [PERSON]'s aphasia. Thereby, we proposed a mathematical model to evaluate participants' performance levels in ASR free features. Subsequently, scores obtained from the mathematical model were analyzed using the diagnosis algorithm. As a result, an objective diagnosis [9] can be made utilizing quantitative observations of pathological speech signals, instead of relying upon subjective and qualitative evaluations by SLPs. In general, a majority of the standardized aphasia assessments follow a manual procedure conducted by a SLP. Conventional aphasia assessments consume a considerable amount of time and explicitly rely on expert knowledge, cultural background, and clinical experience [26]. In this work we incorporated a live auditory model and a recorded auditory model. The average assessment duration using a live auditory model was 396.2 seconds for the naming task and 199.8 seconds for the repetition task. The recorded auditory model took only 375.6 seconds and 180.7 seconds, respectively. Paired sample \(t\)-test confirmed a significant difference in assessment duration for both naming [\(t\) (29) = 10.66, (\(P\) value \(<\) 0.05)] and repetition [\(t\) (29) = 11.12, (\(P\) value \(<\) 0.05)] tasks with the aphasic sample. Hence, we can claim that using a recorded model improves the time efficiency of the assessment process, while mitigating the potential bias of the SLP. Figure 7 depicts assessment time variation between live and auditory models for naming and repetition tasks. Figure 7: Time spent on confrontation naming and repetition tasks using live auditory model (L) and pre-recorded auditory model (R). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{
\begin{tabular}{c} **Single Word** \\ **Accuracy (\%)** \\ \end{tabular} } & \multicolumn{3}{c}{**Simple Command Accuracy (\%)**} \\ \cline{3-5}  & & **Objects** & **Sequences** & **Actions** \\ \hline Anomic (18) & 72.5 & 46.4 & 42 & 41.1 \\ [PERSON]’s (12) & 45 & 29.9 & 27.9 & 25.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance analysis summary of comprehension task. Moreover, hands on experience in clinical assessment procedures significantly influences the diagnosis. Generally, SLPs diagnose language disorders adhering to a standardized assessment kit. Nevertheless, most of the SLPs in non-English speaking countries follow non-standardized assessments due to lack of cultural appropriateness and language differences. The non-standardized assessments do not consider normative data. Instead, they determine isolated skill levels of a person despite the other contributing factors [27], subsequently reducing the benefits of the assessment procedure. Furthermore, the manual assessment process results in multiple loopholes. The major drawback of the manual assessment is the subjectivity of the diagnosis process. It is said to be subjective since the outcome highly depends on the experience, exposure, and perception of the SLP. Moreover, it has been suggested that a computerized approach could be used to minimize the subjectivity of the manual assessment procedure [9].