## 1 Introduction
Based on a subset of ADReSSo-2021 dataset (166 audio recordings), text-based features outperformed audio-based features in Ref. [23] (83.7% accuracy) and Ref. [32] (81.6% accuracy). Some recent studies indicate that a fusion of acoustic and linguistic features can improve AD prediction accuracy [23; 33; 34; 35]. [PERSON] [23] reported 83.7% accuracy by fusing acoustic features (IS10 paraling, fine-tuned wav2 vec 2.0) and deep linguistic features (extracted using fine-tuned BERT) using an SVM classifier on the ADReSSo-2021 dataset. On the same dataset, Ref. [35] reported an accuracy of 81.6% by fusing linguistic and acoustic features. Ref. [36] utilized a C-Attention network trained on a combination of both linguistic features and acoustic embeddings to achieve an accuracy of 80.2%. However, similar to [30], Ref. [34] reported that both text features and fusion features achieved the same accuracy. Further, Ref. [34] reported an accuracy of 81.2% for both text features (Transformer-XL) and bimodal fusion features (ensembled output) on ADReSS-2020 [28] dataset. On this dataset, Ref. [33] achieved 75.0% AD classification accuracy by fusing acoustic-based models and transcript based models, compared to 72.9% and 58% accuracies of unimodal transcript-only and acoustic-only models, respectively. Furthermore, a fusion model investigated by [37] used a BiLSTM model with highway layers using linguistic (words, word probabilities), disfluency features, pause information and a variety of acoustic features to achieve an accuracy of 84.0% based on the ADReSSo-2021 dataset (the focus of this study). Many deep learning (DL) models are now widely used in speech recognition research because they allow for the implementation of multiple layers that capture information at different granularity levels. Improved prediction accuracy scores have been reported for AD classification tasks using deep learning approaches. Based on the ADReSS-2021 dataset (the focus of our study), [PERSON]. [16] used acoustic embeddings from pretrained models (trill, allosaurus, and wav2 vec 2.0), achieving an accuracy of 78.9% using wave2 vec 2.0. Using VGGish deep acoustic embeddings (ADReSS-2020 dataset) combined with other feature aggregation methods, such as Fisher Vector encodings (FVs) and Bag-of-Audio-Words (BoAW), [PERSON]. [38] achieved an accuracy of 85.4%. [PERSON]. [39] attained an accuracy of 83.3% using fine-tuned Bidirectional Encoder Representations from Transformers (BERT) based on the ADReSS-2020 dataset. For automatic AD identification from continuous speech, [PERSON]. [40] utilized perplexity characteristics collected from N-gram language models and achieved 84.5% accuracy. [PERSON]. [41] investigated both language features retrieved from the transcripts and encoded pauses and reported 89.6% accuracy by using pretrained language models along with pause encoding using the ADReSS-2020 dataset. Using this dataset, [PERSON]. [42] achieved 83.3% accuracy by utilizing multi-layered perceptrons (MLP) and recurrent neural networks (RNN) while modelling several audio and linguistic characteristics. By combining the text embeddings (both word level and phoneme level) with audio features, [PERSON]. [43] reported AD classification accuracy of 79.1%. They also used phoneme-level embeddings to train deep learning text systems on their small ADReSS-2020 dataset due to concerns of frequent overfitting. [PERSON]. [44] implemented transformer-based models (ALBERT, XLNet, RoBERTa, BioBERT, BioClinical-BERT, ConvBERT, BERT) on the ADReSS-2020 dataset and achieved an accuracy of 87.5% using BERT text embeddings. Previous studies, however, did not have the opportunity to compare these models on the same dataset. Therefore, to meaningfully assess the efficacy of the various approaches already deployed, here we report a survey comparing multi-modal feature spaces engaging multi-model and multi-feature cues in addressing Alzheimer's Dementia based solely on the same dataset: ADReSSo-2021 [25].