π€ AI Summary
This study addresses the challenge of early Alzheimerβs disease (AD) detection in settings where neuroimaging or speech transcription tools are unavailable. The authors propose the first lightweight, speaker-independent screening method that operates solely on raw spontaneous speech without requiring transcriptions. Their approach employs WebRTC for voice activity detection and manually extracts a 99-dimensional acoustic-temporal feature set encompassing pauses, fluency, spectral characteristics, prosody, and MFCCs along with their dynamic derivatives. Using an RBF kernel SVM classifier evaluated on the DementiaBank Pitt Corpus under 30 speaker-independent splits, the method achieves an average AUC of 0.674, with a best single-split AUC of 0.742 (accuracy: 0.657), thereby demonstrating the feasibility of detecting AD using only untranscribed audio.
π 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 Ξ and ΞΞ. 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.