Beyond surface form: A pipeline for semantic analysis in Alzheimer's Disease detection from spontaneous speech

• URL: http://arxiv.org/abs/2512.13685v1
• Date: Mon, 15 Dec 2025 18:59:49 GMT
• Title: Beyond surface form: A pipeline for semantic analysis in Alzheimer's Disease detection from spontaneous speech
• Authors: Dylan Phelps, Rodrigo Wilkens, Edward Gow-Smith, Lilian Hubner, Bárbara Malcorra, César Rennó-Costa, Marco Idiart, Maria-Cruz Villa-Uriol, Aline Villavicencio,
• Abstract summary: Alzheimer's Disease (AD) is a progressive neurodegenerative condition that adversely affects cognitive abilities.<n>Language models show promise as a basis for screening tools for AD, but their limited interpretability poses a challenge.<n>We introduce a novel approach where texts surface forms are transformed by altering syntax and vocabulary while preserving semantic content.
• Score: 4.447462467582385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition that adversely affects cognitive abilities. Language-related changes can be automatically identified through the analysis of outputs from linguistic assessment tasks, such as picture description. Language models show promise as a basis for screening tools for AD, but their limited interpretability poses a challenge in distinguishing true linguistic markers of cognitive decline from surface-level textual patterns. To address this issue, we examine how surface form variation affects classification performance, with the goal of assessing the ability of language models to represent underlying semantic indicators. We introduce a novel approach where texts surface forms are transformed by altering syntax and vocabulary while preserving semantic content. The transformations significantly modify the structure and lexical content, as indicated by low BLEU and chrF scores, yet retain the underlying semantics, as reflected in high semantic similarity scores, isolating the effect of semantic information, and finding models perform similarly to if they were using the original text, with only small deviations in macro-F1. We also investigate whether language from picture descriptions retains enough detail to reconstruct the original image using generative models. We found that image-based transformations add substantial noise reducing classification accuracy. Our methodology provides a novel way of looking at what features influence model predictions, and allows the removal of possible spurious correlations. We find that just using semantic information, language model based classifiers can still detect AD. This work shows that difficult to detect semantic impairment can be identified, addressing an overlooked feature of linguistic deterioration, and opening new pathways for early detection systems.

Related papers

• Linguistic Indicators of Early Cognitive Decline in the DementiaBank Pitt Corpus: A Statistical and Machine Learning Study [4.417564179511245]
  This study analyzes spontaneous speech transcripts from the DementiaBank Pitt Corpus using three linguistic representations.<n> syntactic and grammatical features retain strong discriminative power even in the absence of lexical content.<n>This study supports the use of linguistically grounded features for transparent and reliable language-based cognitive screening.
  arXiv  Detail & Related papers  (2026-02-11T16:53:57Z)
• How a Bit Becomes a Story: Semantic Steering via Differentiable Fault Injection [1.690922615975256]
  This work investigates how low-level, bitwise perturbations (fault injection) can influence the semantic meaning of its generated descriptions.<n>In image captioning models, a single flipped bit might subtly alter how visual features map to words, shifting the entire narrative an AI tells about the world.<n>We design a differentiable fault analysis framework, BLADE, that uses gradient-based sensitivity estimation to locate semantically critical bits.
  arXiv  Detail & Related papers  (2025-12-09T04:04:19Z)
• Hallucination Benchmark for Speech Foundation Models [33.92968426403491]
  Hallucinations in automatic speech recognition (ASR) systems refer to fluent and coherent transcriptions produced by neural ASR models that are completely unrelated to the underlying acoustic input (i.e., the speech signal)<n>This apparent coherence can mislead subsequent processing stages and introduce serious risks, particularly in critical domains such as healthcare and law.<n>We introduce SHALLOW, the first benchmark framework that systematically categorizes and quantifies hallucination phenomena in ASR along four complementary axes: lexical, phonetic, morphological, and semantic.
  arXiv  Detail & Related papers  (2025-10-18T16:26:16Z)
• Semantic Visual Anomaly Detection and Reasoning in AI-Generated Images [96.43608872116347]
  AnomReason is a large-scale benchmark with structured annotations as quadruple textbfAnomAgent<n>AnomReason and AnomAgent serve as a foundation for measuring and improving the semantic plausibility of AI-generated images.
  arXiv  Detail & Related papers  (2025-10-11T14:09:24Z)
• Linguistic and Audio Embedding-Based Machine Learning for Alzheimer's Dementia and Mild Cognitive Impairment Detection: Insights from the PROCESS Challenge [0.0]
  Speech, encompassing both acoustic and linguistic dimensions, offers a promising non-invasive biomarker for cognitive decline.<n>We present a machine learning framework for the PROCESS Challenge, leveraging both audio embeddings and linguistic features derived from spontaneous speech recordings.
  arXiv  Detail & Related papers  (2025-10-02T06:54:55Z)
• Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease [52.46922921214341]
  Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society.<n>We devised an explainable and effective feature set that leverages the visual capabilities of a large language model (LLM) and the Term Frequency-Inverse Document Frequency (TF-IDF) model.<n>Our new features can be well explained and interpreted step by step which enhance the interpretability of automatic AD screening.
  arXiv  Detail & Related papers  (2024-11-28T05:23:22Z)
• Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection [4.961581278723015]
  Alzheimer's disease (AD) stands as the predominant cause of dementia, characterized by a gradual decline in speech and language capabilities. Recent deep-learning advancements have facilitated automated AD detection through spontaneous speech. Common transcript-based detection methods directly model text patterns in each utterance without a global view of the patient's linguistic characteristics.
  arXiv  Detail & Related papers  (2024-09-19T07:58:07Z)
• Pixel Sentence Representation Learning [67.4775296225521]
  In this work, we conceptualize the learning of sentence-level textual semantics as a visual representation learning process. We employ visually-grounded text perturbation methods like typos and word order shuffling, resonating with human cognitive patterns, and enabling perturbation to be perceived as continuous. Our approach is further bolstered by large-scale unsupervised topical alignment training and natural language inference supervision.
  arXiv  Detail & Related papers  (2024-02-13T02:46:45Z)
• Agentivit\`a e telicit\`a in GilBERTo: implicazioni cognitive [77.71680953280436]
  The goal of this study is to investigate whether a Transformer-based neural language model infers lexical semantics. The semantic properties considered are telicity (also combined with definiteness) and agentivity.
  arXiv  Detail & Related papers  (2023-07-06T10:52:22Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• Semantic Coherence Markers for the Early Diagnosis of the Alzheimer Disease [0.0]
  Perplexity was originally conceived as an information-theoretic measure to assess how much a given language model is suited to predict a text sequence. We employed language models as diverse as N-grams, from 2-grams to 5-grams, and GPT-2, a transformer-based language model. Best performing models achieved full accuracy and F-score (1.00 in both precision/specificity and recall/sensitivity) in categorizing subjects from both the AD class and control subjects.
  arXiv  Detail & Related papers  (2023-02-02T11:40:16Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes.<n>We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.