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Maria F. Alcala-Durand

Publications and source records attributed to Maria F. Alcala-Durand.

2 recordsLinked to original sources

Counting Closures in Spanish Trills: A Multi-Corpus Acoustic Study

The Spanish trill /r/ is canonically described as a short sequence of lingual closures, yet large-scale acoustic evidence across corpora is scarce, and automatic counters locating envelope peaks tend to conflate each closure with its release. We present a closure-based detector that locates closures gated by a quality filter and cross-checked against an independent autocorrelation-based period estimator. Applied to 3,560 well-formed (voiced, periodic) trill tokens from 356 speakers across six Spanish corpora, the detector yields a median of two closures and an inter-closure period near 36ms, matching the descriptive literature on all corpora. At the speaker level, phonotactic context is the only factor with a robust, medium effect: onset trills (word-initial and post-/n,l,s/) show more closures than intervocalic rr. We find no robust evidence of a sex effect once closures are counted directly. We report reference values and release a reproducible measurement pipeline for Spanish trills.

eess.AS↗

Automatic Screening of Parkinson's Disease from Visual Explorations

Eye movements can reveal early signs of neurodegeneration, including those associated with Parkinson's Disease (PD). This work investigates the utility of a set of gaze-based features for the automatic screening of PD from different visual exploration tasks. For this purpose, a novel methodology is introduced, combining classic fixation/saccade oculomotor features (e.g., saccade count, fixation duration, scanned area) with features derived from gaze clusters (i.e., regions with a considerable accumulation of fixations). These features are automatically extracted from six exploration tests and evaluated using different machine learning classifiers. A Mixture of Experts ensemble is used to integrate outputs across tests and both eyes. Results show that ensemble models outperform individual classifiers, achieving an Area Under the Receiving Operating Characteristic Curve (AUC) of 0.95 on a held-out test set. The findings support visual exploration as a non-invasive tool for early automatic screening of PD.

q-bio.NC↗