## 1 Introduction  
Alzheimer's Dementia (AD) is a neuro-degenerative disease [1], where language and speech decline [2, 3] can manifest years before other functions, such as behavior, memory, and sensory-motor skills, are compromised [2, 4]. A wide range of speech and language impairments are well-documented and the focus of several review papers (e.g., [5, 6, 7, 8], amongst others). Analysing spontaneous speech may afford timely intervention upon diagnosis of AD in its early stages. With the rise of machine learning (ML) tools in the last decade, automatic speech analysis exploiting acoustic features for the screening, early detection and intervention of AD are becoming more robust and more practical to implement [9, 10, 11]. Furthermore, text feature extraction techniques due to breakthroughs in natural language processing (NLP)tools [12], pretrained transformer-based language models [13; 14] and automatic speech recognition (ASR) [15; 16] methods have shown promising results in predicting AD. Accordingly, speech-based approaches for AD prediction rely on acoustic features [16; 17; 18; 19], text representations [20; 21; 22] or a fusion of both [23; 24]. The authors in Ref. [17] compared conventional acoustic features (fundamental frequency, jitter, shimmer, etc. ), pretrained acoustic embeddings using wav2 vec 2.0 and a combination of both (by simply concatenating the representations from pretrained models and conventional acoustic features) on the ADReSSo-2021 (Alzheimer's Dementia Recognition through Spontaneous Speech only) dataset [25]. In addition, Ref. [17] investigated Logistic Regression (LR), Support Vector Machines (SVM), Neural Networks (NN), and Decision Trees (DT), reporting 67.6% accuracy on the combined representation of conventional acoustic features and pretrained acoustic embeddings (using SVM classifier). ADReSSo-2021 dataset [25] is also chosen for our investigation and comprises of 166 and 71 audio recordings in the training set and testing set, respectively. Using the same dataset, [PERSON]. [16] achieved 78.9% accuracy using pretrained acoustic embeddings. Notably, [PERSON]. [18] studied AD classification using only acoustic features on a DementiaBank dataset [26; 27] (473 recordings: 233 speech samples from 97 cognitively normal (CN) and 240 samples from 167 AD patients) to report high accuracies of 94.7%, 92.3% and 90.9% using Bayesian Networks (BN), Meta-Bagging (MB) and Random Forest (RF) classifiers, respectively. On the other hand, [PERSON]. [20] achieved an accuracy of 81.0% by utilizing only linguistic features from textual transcripts of the spontaneous speech (with pretrained transformer-based models) based on the smaller ADReSS-2020 [28] dataset (audio recordings in training set: 108; testing set: 48) [28]. Using only transcripts of this dataset, [PERSON]. [22] achieved an accuracy of 83.3% by using FastText-based classifiers to which bigrams and trigrams were appended to the input transcription (which included discourse markers such as \"um\" and \"uh\"). Indeed, incidental comparisons between acoustic-based features and text-based features in dementia classification showed that text-based features performed better than acoustic-based features [24; 29; 30; 31; 32]. More specifically, Ref. [24] classified AD patients with 85.0% accuracy using language-only features as compared to 65.0% using acoustic-only features on the ADReSS-2020 test dataset. On the same dataset, [PERSON]. [29] achieved 85.4% accuracy with BERT text embeddings (SVM classifier), whereas the acoustic feature accuracy using i-vectors (kNN classifier) was only 56.3%. Based on the ADReSSo-2021 dataset, Ref. [31] achieved the highest published accuracy of 84.5% (to date) using linguistic-only features, compared to 74.6% accuracy of acoustic-only features. On this dataset, however, Ref. [30] reported identical accuracies of 84.5% obtained for linguistic features as well as fusion of acoustic and linguistic features.