# Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0

arXiv: https://arxiv.org/abs/2501.16201 · HTML: https://mlsanity.com/paper/2501.16201/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [SeamlessM4T: Massively Multilingual & Multimodal Machine Translation](https://mlsanity.com/paper/2308.11596/) (2023)
- [Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations](https://mlsanity.com/paper/2110.14513/) (2021)
- [What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure](https://mlsanity.com/paper/2101.00387/) (2021)
- [To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2008.01551/) (2020)
- [On the Utility of Self-supervised Models for Prosody-related Tasks](https://mlsanity.com/paper/2210.07185/) (2022)
- [Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speech](https://mlsanity.com/paper/2210.11723/) (2022)
- [Connected Speech-Based Cognitive Assessment in Chinese and English](https://mlsanity.com/paper/2406.10272/) (2024)
- [CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speech](https://mlsanity.com/paper/2407.13660/) (2024)
- [Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses](https://mlsanity.com/paper/2306.03443/) (2023)
- [Classifying Dementia in the Presence of Depression: A Cross-Corpus Study](https://mlsanity.com/paper/2308.08306/) (2023)
- [Alzheimers Dementia Detection using Acoustic & Linguistic features and Pre-Trained BERT](https://mlsanity.com/paper/2109.11010/) (2021)
- [Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Features](https://mlsanity.com/paper/2206.05018/) (2022)
- [Soft-Weighted CrossEntropy Loss for Continous Alzheimer's Disease Detection](https://mlsanity.com/paper/2402.11931/) (2024)

## Abstract (arXiv)

This study explores a multi-lingual audio self-supervised learning model for detecting mild cognitive impairment (MCI) using the TAUKADIAL cross-lingual dataset. While speech transcription-based detection with BERT models is effective, limitations exist due to a lack of transcriptions and temporal information. To address these issues, the study utilizes features directly from speech utterances with W2V-BERT-2.0. We propose a visualization method to detect essential layers of the model for MCI classification and design a specific inference logic considering the characteristics of MCI. The experiment shows competitive results, and the proposed inference logic significantly contributes to the improvements from the baseline. We also conduct detailed analysis which reveals the challenges related to speaker bias in the features and the sensitivity of MCI classification accuracy to the data split, providing valuable insights for future research.