## 5 Conclusions  
An approach for assessing audio recordings of spontaneous speech utterances related to Alzheimer's Dementia, utilizing a multi-model machine learning strategy, is presented. The study analysed the effectiveness of various features extracted from audio recording and text derived from the audio, employing 16 different models to assess their relative performance. All models trained on various distinct feature sets demonstrated accuracy above chance levels. Overall, text-based features extracted from the transcribed audio outperformed the audio-based features. The best performing model was achieved by modelling the RoBERTa text embeddings, which attained an accuracy of 88.7%, achieving near-perfect classification for AD, identifying 34 out of 35 cases. This represents a 4.2% improvement surpassing other state-of-the-art models for AD classification trained on the same ADReSSo-2021 dataset. When comparing the level of granularity (resolution) in the feature extraction process, frame-level audio features generally outperform their file-level counterparts. However, as the number of features increase, such as with Emobase-Large, the results become more comparable. This suggests that a sufficient number of low-level descriptors can effectively represent both AD and CN classes. In contrast, while file-level text embeddings have produced higher accuracy than frame-level word embeddings, this trend cannot be generalized since only one model was tested at the latter level. The four original feature extraction methods we proposed, namely Speech/Silence, Energy-Time plot, Keg of Text Analytics and Keg of Text Analytics-Extended, demonstrated reasonable accuracy. The image-based Energy-Time plots may offer a new and promising avenue of dementia detection and invites further investigation. While their performance may not yet match off-the-shelf feature sets, additional exploration and fine-tuning of these four original feature extraction methods could lead to further improvements. This investigation suggests that the transcribed textual data can produce meaningful word sequences that lead to contrasting results in text feature analysis. This is particularly helpful in classifying AD and CN patients, as factors such as semantic distance, vocabulary usage and grammar structures tend to be simpler for dementia patients. While transcribing English audio datasets is relatively straightforward, transcription resources for other languages may not be as easily available nor as mature and robust, making the text-based approach sensitive to the linguistic context of the target speaker. On the other hand, in an audio-based approach, data are not constrained by carrier language, making it more generalizable and useful for developing AD classification models that are more universal, as long as good signal-to-noise ratios of the speech utterances are captured. In practical terms, however, it is expected that the optimal solution is to implement a combination of both approaches to best reconcile differences arising from the context. This study provides insight into the effectiveness of machine learning techniques trained on both textual and audio data for a given dataset, which is an improvement over previous studies that focused on only one (or one of the many) aspect(s) of the feature space or model selection. Insights from this study provide a fast and non-invasive means of reliable screening for Alzheimer's Dementia, assessing its severity and monitoring its progression. Profoundly, outcomes from the frame-level audio features hint at its potential to be generalizable across languages. Conceptualization, P.P., B.B.T., C.J.C. and J.-M.C. ; methodology, P.P., B.B.T., C.J.C., J.-M.C., C.M.Y.L. and J.M. ; software, P.P., B.B.T., C.J.C., C.M.Y.L. and J.M. ; validation, P.P., B.B.T., C.J.C. and J.-M.C. ; investigation, P.P., B.B.T., C.J.C., J.-M.C., C.M.Y.L. and J.M. ; data curation, P.P., B.B.T., C.J.C. and J.-M.C. ; writing--original draft preparation, P.P., B.B.T., C.J.C., J.-M.C. and J.M.