## 4 Discussion
Frame-level features, such as sub-word or phoneme-level representations, are suitable for training on small datasets and have been shown to have better dementia classification accuracy than file-level features (such as word or sentence level) [43]. On the other hand, file-level features have the benefit of larger temporal samples and increased opportunities to rely on cues that arise randomly or infrequently [41]. Overall, text modality performs better on average (file-level + frame-level) than audio. This may suggest that with the onset of AD cognitive decline affects linguistic capacity more sensitively and may be apparent earlier than physiological decline. Given that factors, such as semantic distance, lexical resource and grammar structure, may become simpler as cognitive decline progresses, text-based methods could be more useful and provide greater insight into differentiating AD from CN, resulting in higher detection accuracy. However, text-based approaches require faithful and robust transcription processing as an additional step, complicating the analysis. Furthermore, many transcription services currently available automatically omit non-semantic utterances (such as _mmm_, _uh_, _um_ and _ah_) which may in fact further enhance detection accuracy [41; 62]. Most of the best performing AD classification approaches investigating the ADReSSo-2021 dataset [25] are trained on either text features alone or a fusion of features, such as text and audio [25; 37], text and disfluency [60] and text and pauses [61], among others. Using solely text, Ref. [31] achieved an accuracy of 84.5%. On the other hand, several studies [25; 30; 35; 37; 60; 61] also reported good accuracies by using fusion approaches, such as integrating audio and text data, with accuracies ranging from 78.8% to 84.5%. An audio-only study by [16] reported 78.9% accuracy using wave2 vec 2.0 embeddings, which is comparable to our best-performing audio-only model accuracy of 78.9% using VGG and Prosody features. Our best performing approach using DNN was trained on RoBERTa text embeddings and offers comparable AD detection accuracy scores while still outperforming the existing state-of-the-art approaches on the ADReSSo-2021 dataset. While more approaches on other datasets exist in the literature, comparing them with this study will not offer a fair comparison. In our study, fine-tuned pretrained RoBERTa embeddings (using automatically transcribed text) achieved the highest accuracy (to date) score of 88.7%, offering a 4.2% improvement over Ref. [30] and Ref. [31] (both reported 84.5% accuracy). Thus, although on prima facie, our study may seem to suggest that text-based methods outperform audio-based methods, the successful implementation of text-based methods depends sensitively on the context in question (such as carrier language, choice and availability of reliable transcription, among others). Audio-based methods, on the other hand, will offer greater flexibility less sensitively to the context. As long as a good signal-to-noise ratio is achieved in recording the speaker's utterances, analysis can still proceed with some reliability, agnostic to the carrier language(s) involved. Lastly, depending again on the audio context and linguistic requirements, a simple voting- and/or fusion-model can then be implemented to reconcile the mutli-modal multi-model multi-feature approach to yield an optimal AD/CN classification outcome. However, this approach was not tested in our investigation due to the absence of a validation dataset. Thus, although text generally has better accuracy than audio, it is limited by the reliability of transcription services. Audio features, on the other hand, would not be constrained by the limitations of carrier/target language per se. It should also be noted that the performance of the various models in this study is limited inherently by the reliability of the provided clinical labels and the quality and consistency of audio recordings provided in the dataset.