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

arXiv 2501.16201 ↗First posted 27 Jan 2025eess.AS cs.CL cs.SDcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 27 Jan 2025.

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References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  3. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
  4. 2021W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Trainingcited by 340
  5. 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 293
  6. 2023SeamlessM4T: Massively Multilingual & Multimodal Machine Translationcited by 124
  7. 2021Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representationscited by 81
  8. 2021What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structurecited by 52
  9. 2020To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detectioncited by 49
  10. 2022On the Utility of Self-supervised Models for Prosody-related Taskscited by 42
  11. 2022Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speechcited by 30
  12. 2024Connected Speech-Based Cognitive Assessment in Chinese and Englishcited by 13
6 more
  1. 2024CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speechcited by 8
  2. 2023Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pausescited by 5
  3. 2023Classifying Dementia in the Presence of Depression: A Cross-Corpus Studycited by 5
  4. 2021Alzheimers Dementia Detection using Acoustic & Linguistic features and Pre-Trained BERTcited by 4
  5. 2022Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Featurescited by 3
  6. 2024Soft-Weighted CrossEntropy Loss for Continous Alzheimer's Disease Detectioncited by 1

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