The results indicate that transcript-free spectro-temporal and fluency-related cues can facilitate speaker-independent Alzheimer's disease screening from raw audio, establishing a practical foundation for deployment-oriented research.
Abstract
It is still hard to find Alzheimer's disease (AD) early, especially when neuroimaging is expensive or tools that depend on language are not available. Spontaneous speech provides a non-invasive signal; however, numerous current methodologies depend on transcripts/ASR or computationally intensive deep models. We offer a simple, audio-only baseline for detecting AD using 176 Cookie Theft recordings from the DementiaBank Pitt corpus (88 AD, 88 controls). WebRTC voice activity detection (VAD) is used to separate speech from non-speech. We take out 99 hand-crafted acoustic-temporal features, including pause and fluency statistics, spectral/prosodic descriptors, and MFCC summaries with {\Delta} and {\Delta}{\Delta}. Evaluation is performed using a stringent speaker-independent GroupShuffleSplit,documenting performance across 30 iterations. A lightweight SVM with an RBF kernel gets an average AUC of 0.674 across runs. For example, a single split has an AUC of 0.742 and an accuracy of 0.657. We also present an exploratory compact-feature analysis utilizing a Top-20 subset ranked by Random Forest importance; since selection is not nested within training splits, these results may be overly optimistic and are not employed for primary conclusions (AUC 0.719). The results indicate that transcript-free spectro-temporal and fluency-related cues can facilitate speaker-independent Alzheimer's disease screening from raw audio, establishing a practical foundation for deployment-oriented research.
A layer-wise analysis of nine SSL speech backbones using a low-capacity logistic regression probe reveals that the transferred discriminative signal lacks pathological specificity, highlighting critical limitations that must be addressed before speech-based pathology recognition models can be reliably deployed in clinical settings.
S. Kopar, Sam Gijsen, Abner Hernandez et al.· 0 citations
Parkinsons Disease (PD) is a progressive neurodegenerative disorder which significantly affects motor function, daily coordination and verbal communication. Speech-based biomarkers provide a non-invasive and scalable approach to early detection, as dysphonia is one of the earliest and most consistent clinical markers of PD. The dataset used in this study is publicly available and consists of 756 voice recordings from 252 subjects (188 with PD and 64 neurologically healthy controls) with a wide range of acoustic parameters such as Mel-Frequency Cepstral Coefficients (MFCCs), energy-based parameters, and higher-order statistical derivatives. After systematic preprocessing and z-score normalisation, five machine learning classifiers were tested: K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB) under a subject-independent, GroupKFold cross-validation protocol. The KNN classifier performed best overall with an accuracy of 92.10%, F1 score of 94.50%, and a precision rate of 98.09%, reducing the number of false positive diagnoses. To overcome the lack of interpretability of black-box predictive models, SHapley Additive exPlanations (SHAP) were used to explain the contribution of each feature to the prediction of an individual. The most diagnostically salient acoustic biomarkers were identified as features from the SHAP analysis: std delta delta log energy, the first Mel-Frequency Cepstral Coefficient, and Tunable Q-Factor Wavelet Transform (TQWT). This work introduces a machine learning framework that is both reproducible and clinically interpretable, combining high predictive accuracy with transparent, physiologically grounded decision logic.
D. Howlader, T. Ahmed, M. M. Rahman· medRxiv· 0 citations
Speech-based Alzheimer's disease (AD) detection increasingly relies on speech-enhanced and curated versions of the Pitt Corpus, where speech enhancement, sample selection, and demographic balancing are often treated as beneficial preprocessing steps. However, whether these transformations improve real-world AD detection or instead affect model generalization and prediction behavior remains unclear. In this work, we revisit the role of speech preprocessing and dataset curation across widely used benchmarks for speech-based AD detection. We evaluate the speech quality of different datasets, the cross-dataset generalization of multiple deep learning models under matched and mismatched enhancement settings, and the behavior of several recent large audio-language models (LALMs). Experimental results show that across multiple supervised speech models, speech-enhanced datasets often improve in-domain performance while reducing robustness in cross-domain evaluation. Matched enhancement between training and test data alleviates, but does not eliminate, this degradation. LALMs show a similar sensitivity: enhanced datasets induce stronger class imbalance and prediction shifts than unprocessed data. These results suggest that speech preprocessing and dataset curation can substantially influence downstream AD detection behavior, indicating that ``cleaner''speech datasets are not necessarily more reliable for real-world AD detection.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that severely impairs motor control and quality of life. Conventional diagnostic approaches such as clinical motor assessment and neuroimaging are expensive, invasive, and lack sensitivity for early-stage detection. However, subtle alterations in vocal characteristics, including tremors, pitch instability, and hoarseness, can manifest years before motor symptoms become evident, offering a promising non-invasive biomarker. To address these challenges, we propose a novel ensemble-driven machine learning framework that leverages acoustic features from speech signals for early PD detection. Using an open-access voice dataset, 22 clinically validated vocal biomarkers were extracted. Six supervised models were trained and evaluated, with a custom Stacking Classifier achieving superior performance with an accuracy of 97.77%, precision of 98.39%, recall of 95.31%, and an F1-score of 96.83%. This ensemble method integrates the strengths of diverse learners to ensure stable and generalizable predictions. Furthermore, the system incorporates a post-diagnostic chatbot powered by a Large Language Model (LLM), enabling personalised support and guidance. The proposed framework is non-invasive, scalable, and well-suited for telemedicine, representing a significant step toward accessible early-stage PD screening.
Early diagnosis is critical for effective management of Alzheimer's disease (AD). While prior studies have shown that speech features can be indicative of AD, most existing work consolidates multiple biomarkers, making it difficult to isolate the contribution of individual features.
This study systematically isolates individual speech biomarkers to quantify their distinct contributions to AD classification performance of language models (LMs) and determine whether targeted biomarker selection improves over consolidated feature sets.
We processed speech transcriptions from DementiaBank to surface discriminatory speech biomarkers—verbal pauses, disfluencies, and unintelligible words. We then fine-tuned and evaluated lightweight LMs (BERT, AlBERT, and DistilBERT) on these biomarker-conditioned transcripts for automatic AD classification.
Pauses emerged as the most discriminatory speech biomarker (F1 = 0.8326), significantly outperforming the No-biomarker baseline and other biomarkers. Combining all biomarkers degraded performance relative to pauses alone on average across models, though the effect was model-dependent: BERT's All-biomarkers condition exceeded its own No-biomarker baseline, suggesting that feature combination benefits higher-capacity models. BERT yielded the best performance (F1 = 0.8154) across conditions.
Selective use of speech biomarkers such as pauses can meaningfully improve AD detection with lightweight LMs, suggesting that targeted biomarker selection may offer a more interpretable and clinically actionable path than broad feature consolidation.
Zara Khanna, Dean Ho, A. Remus et al.· Frontiers in Artificial Inte...· 0 citations
Depression is among the leading causes of disability worldwide, yet its detection continues to rely heavily on subjective clinical interviews and self-report instruments that are difficult to scale, particularly in low-resource regions. This paper presents the design, implementation, and empirical validation of a multimodal fusion neural network for automatic depression detection from speech and text, developed with specific attention to closing the near-total absence of African research contributions in this rapidly growing area of computing. The proposed system extracts spectral-prosodic acoustic features from voice recordings using a convolutional encoder, and contextual semantic features from transcribed text using a transformer-based language encoder, before an attention gate learns to weight the relative contribution of each modality per instance ahead of a fully connected classifier. The architecture was implemented and validated end-to-end as a feasibility study on two accessible, weak-label proxy corpora: the RAVDESS acted-emotion speech dataset, with sad and calm recordings relabeled as a depression-like class, and the dair-ai/emotion text corpus, with sadness and fear posts relabeled likewise. On held-out test data, the fusion model reached 90.28% accuracy and 0.9609 AUC-ROC, and, most notably, recovered the positive-class recall that the audio-only model lost almost entirely (21.05% versus 84.21%), demonstrating that the attention-gated fusion mechanism functions as designed. This paper reports the background, problem definition, related work, the complete mathematical formulation of the implemented pipeline, the empirical results of this proxy validation, and a discussion of what they do and do not establish, closing with concrete recommendations for advancing the work toward a clinically meaningful, Africa-relevant screening tool.
Bulus Gago Kindness, Yebduya Garleya Lodiya, Kamak Yamlach Shedrach et al.· International journal of res...· 0 citations
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