## 4 Discussion  
This study presents a systematic comparison of various approaches and methods, thus allowing us some insight into the speech information that should be considered depending on the speech sampling context. If the audio quality is amenable to faithful transcription (low background noise, good speaker clarity, monolingual and well-documented accents), our study shows that file-level text is most effective, with the BERT family performing rather satisfactorily at 86.2 \(\pm\) 2.2%. However, care must be taken with file-level text as XLNet feature (67.6%) offered the second lowest accuracy among the 16 features studied. In contrast, if the audio quality is good (high signal-to-noise ratio), but the speaker does not enunciate clearly or perhaps speaks in a way not compatible with automated text transcription (e.g., mixed languages used, poorly documented accent), the results guide us to suggest that frame-level analysis is then preferred. It can be expected that phonatory dynamics captured in short time frames may contain enough inherent information about the speaker's psycho-motor health. Further, such an approach relying on using audio feature sets alone has the added benefit of not being limited to a particular carrier language since it should reflect the speaker's cognitive/physiological state without the extra burden \begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Best Performing Modality** & **Highest Accuracy \% (Model)** \\ \hline
[36] & Fusion (Audio + Text) & 80.2 (C-Attention Network) \\
[30] & Fusion (Audio + Text) & 84.5 (LR) \\
[16] & Audio & 78.9 (DNN) \\
[37] & Fusion (Audio + Text) & 84.0 (BiLSTM) \\
[31] & Text & 84.5 (BERT\({}_{large}\)) \\
[60] & Fusion (Audio + Text) & 83.1 (Ensemble) \\
[61] & Fusion (Audio + Text) & 83.1 (DNN) \\
[35] & Fusion (Audio + Text) & 81.6 (Ensemble) \\
[25] (Baseline) & Fusion (Audio + Text) & 78.8 (Ensemble) \\ This study & Text & 88.7 (DNN) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with other studies reporting from the ADReSSo-2021 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Resolution (Modality)**} & \multicolumn{1}{c}{\multirow{2}{*}{**Feature**}} & \multicolumn{1}{c}{\begin{tabular}{c} **Accuracy (\%)** \\ **(Correct CN Class/Total CN Class,** \\ **Correct AD Class/Total AD Class)** \\ \end{tabular} } \\ \hline  & \multicolumn{2}{c}{eGeMAPS} & 67.6 (26/36, 22/35) \\  & \multicolumn{2}{c}{Emobase-Large} & 77.5 (30/36, 25/35) \\ file-level (Audio) & \multicolumn{2}{c}{Emobase} & 70.4 (29/36, 21/35) \\  & \multicolumn{2}{c}{Speech/Silence} & 66.2 (25/36, 22/35) \\  & \multicolumn{2}{c}{Energy-Time plot} & 73.2 (25/36, 23/35) \\ \hline  & \multicolumn{2}{c}{\begin{tabular}{c} Key of Text Analytics \\ Key of Text Analytics-Extended \\ Summed Word Embedding \\ BERT \\ DistilBERT \\ RoBERTa \\ XLNet \\ \end{tabular} } & 76.1 (30/36, 24/35) \\  & \multicolumn{2}{c}{\begin{tabular}{c} Sommed Word Embedding \\ [PERSON] \\ [PERSON] \\ RoBERTa \\ XLNet \\ \end{tabular} } & 73.2 (26/36, 26/35) \\ \hline  & \multicolumn{2}{c}{\
\begin{tabular}{c} OpenSMILE (Prosody) \\ VGG \\ OpenL3 \\ \end{tabular} } & 78.9 (28/36, 28/35) \\  & \multicolumn{2}{c}{
\begin{tabular}{c} VGG \\ OpenL3 \\ \end{tabular} } & 78.9 (31/36, 25/35) \\ \hline frame-level (Text) & \multicolumn{2}{c}{Word Embedding} & 78.9 (28/36, 28/35) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training features and their associated performance are shown. Accuracy is presented as a percentage reflecting the total correct predictions out of total predictions. Accuracy = (True Positive+True Negative)/(True Positive+True Negative+False Positive+False Negative). AD class is positive. AD: Alzheimer’s Dementia; CN: Normal Control. and process of transcription pigeon-holed in a particular carrier language (which may introduce transcription errors compromising the efficacy of a text-based approach).