## 2 Methodology  
### Operational Procedure of the Developed Aphasia Classification System  
The implementation of the proposed aphasia classification system was initiated by compiling a manual assessment. The manual speech assessment was developed to assess confrontational naming, single word repetition, single word comprehension, and simple command comprehension tasks. In manual assessment, a live model carried out the aforementioned tasks. Herein, manual assessment was tested with 5 neurologically healthy adults and 5 adults who had experienced a stroke but had been assessed as non-aphasic to validate the assessment materials, tasks, and instructions. Accordingly, the materials, tasks, and instructions used in the manual assessment were replicated in the software solution. Thus, this guarantees that the hybrid aphasia assessment tool is free from ambiguities. The assessment procedures were conducted by a speech and language pathologist certified at the medical council of Sri Lanka. The study followed a non-invasive, non-destructive, and non-harmful procedure, which only recorded human speech samples via a microphone. The ethical committee of the National Hospital Sri Lanka (Colombo) approved the study, and speech sample recording was performed in accordance with the guidelines of the of ethical committee of National Hospital Sri Lanka (Colombo) and the Institutional Care and Ethical Use Committees of Sri Lanka Institute of Information Technology, Sri Lanka and Kyungil University, Korea. All participants were included with approved consent of participant and/or guardian. Informed consent covered the participant's approval to participate in the language assessment procedure, to share demographic information, to share clinical history, and to audio record assessment speech samples.  
Speech recognition is highly influenced by the frequency and the amplitude of a signal, which are the main components of an acoustic signal. However, these two components alone are insufficient to analyze and suggest a differential diagnosis of a pathological speech signal. Thus, the proposed system requires additional parameters that facilitate differential diagnosis of aphasia. The analysis focused on the number of pathological speech errors, cues given, and time taken to respond. The cues given are categorized into phonemic cues and semantic cues, where these cues as well as test words were included in our laboratory customized user manual. In order to determine a comprehensive relationship between language skills and errors, each error type and cue were handled separately. The clinical diagnosis was taken as the baseline to evaluate the accuracy of the developed system. All participants were clinically diagnosed and clinical diagnosis was blinded to the assessor who evaluated the participants using the proposed algorithm and vice versa. The proposed workflow began with the manual assessment compilation. Manual assessment materials were validated with a pilot test involving 5 neurologically healthy adults and 5 non-aphasic stroke patients. In the softwaredesign process, influential parameters were defined and assigned with scores to derive the math model. Simultaneously, a testing sample and control sample were identified and a clinical diagnosis was performed to use as the baseline for system validation. The materials and instructions of the manual assessment and math model were implemented as a software solution to evaluate participants. Finally, system generated diagnosis was tested with the clinical diagnosis to validate the accuracy of the system. Figure 1 outlines the afore described workflow of the proposed hybrid aphasia inspection tool. Moreover, the numerical algorithm as well as the automated software interface was developed using MATLAB software (Mathworks, MA, USA).