## 3 Results  
All our 16 models performed well above chance (see Table 3), with the poorest performance arising from Speech/Silence with 66.2% accuracy. On the other hand, the best performing model was RoBERTa at an accuracy of 88.7%, followed by DistilBERT at 85.9% and BERT at 84.5%. Looking at the confusion matrix (Figure 5) for RoBERTa, 29 out of 36 CN subjects and 34 out of 35 AD subjects were correctly classified. A comparison between file-level text features and file-level audio features reveals the file-level text features outperform file-level audio features: \(78.8\pm 7.6\%\) vs. \(70.9\pm 4.5\%\), respectively, on average. This observation is true even on a broader scale (overall text vs. overall audio): \(78.8\pm 7.0\%\) vs. \(73.2\pm 4.9\%\), respectively, on average. This agrees with previous observations [24; 30; 43] that text features contain more distinguishing cues than audio features for identifying AD. Regarding automated transcription, whose accuracy depends on the overall intelligibility of utterances recorded, CN cases naturally transcribe richer (due to increased verbosity and word variety) and more faithfully (due to better speech clarity) than AD. Transcriptions that yield meaningful strings of words (or not) will result contrastingly in the text feature analysis (see Figure 3), facilitating classification between AD and CN. Accordingly, we indeed observed our best performances at 88.7%, 85.9% and 84.5% when using BERT-related features. While analyzing other text feature sets, the Key of Text Analytics and Key of Text Analytics-Extended both resulted in identical accuracies of 76.1%, a drop in performance compared to the BERT-related features (which was associated with more AD misclassifications). Finally, the Summed Word Embedding resulted in greater AD and CN misclassifications due to lower accuracy (73.2%) in comparison with Key of Text features and BERT-related text features. Now, frame-level audio features (77.0 \(\pm\) 3.2%) performed better than file-level audio features (70.9 \(\pm\) 4.5%), suggesting that extended audio recordings need not be aggregated, but rather brief speech audio samples representing intrinsic mechanics and dynamics of speaker idiosyncrasies may be preferred, being also easier to collect and execute. Among the various file-level audio features explored, however, Emobase and Emobase-Large yielded accuracies of 70.4% vs. 77.5%, respectively. In this case, Emobase-Large (consisting of a larger feature set) resulted in lower misclassification for both AD and CN. In contrast, eGeMAPS resulted in 67.6% accuracy, which is associated with greater AD and CN misclassification than Emobase (70.4%). The best performing text-based feature at the file-level (RoBERTa) performed 9.8% better than the frame-level (Word Embedding). Conversely, the best audio-based feature at the frame-level (VGG) performed 1.4% better than the best file-level audio feature (Embobase-Large). Extracting deep features using VGG's deep neural networks at the frame-level could be a reason for its higher performance than the conventional acoustic features (low-level descriptors and functionals) of extractors, such as Embase-Large. Mostly, VGG was able to find task-specific features leading to better classification accuracy. Our four original feature extraction methods proposed--Speech/Silence, Energy-Time plot, Keg of Text Analytics, and Keg of Text Analytics-Extended--achieved accuracies of 66.2%, 73.2%, 76.1% and 76.1%, respectively. Although the accuracies of both Keg of Text Analytics-Extended features were 76.1%, the incorrectly classified subjects were not the same. These ab initio results may be currently lower than the well-established features, but with further exploration, we may expect overall improvements in performance. The pretrained BERT, DistilBERT, RoBERTa and XLNet models used in this study achieved accuracies of 84.5%, 85.9%, 88.7% and 67.6%, respectively (Table 3).