The Mason-Alberta Phonetic Segmenter: a forced alignment system based on deep neural networks and interpolation.

Given an orthographic transcription, forced alignment systems automatically determine boundaries between segments in speech, facilitating the use of large corpora. In the present paper, we introduce a neural network-based forced alignment system, the Mason-Alberta Phonetic Segmenter (MAPS). MAPS serves as a testbed for two possible improvements we pursue for forced alignment systems.

Documenting and modeling the acoustic variability of intervocalic alveolar taps in conversational Peninsular Spanish.

This study constitutes an investigation into the acoustic variability of intervocalic alveolar taps in a corpus of spontaneous speech from Madrid, Spain. Substantial variability was documented in this segment, with highly reduced variants constituting roughly half of all tokens during spectrographic inspection. In addition to qualitative documentation, the intensity difference between the tap and surrounding vowels was measured.

Language level predicts perceptual categorization of complex reversible events in children.

Language plays a well-documented role in perceptual object categorization, but little is known about its role in the categorization of complex events. We explored this here with a perspective from age or developmentally appropriate language capacities in neurotypical children between the ages of two and four years (N = 21), and from delayed language development in a clinical group of children (N = 20), whose verbal mental ages (VMA) often fell far below their chronological ages (CAs). All participants watched two demonstrations of a series of transitive events (e.