Automatic Measurement of Pre-aspiration

Pre-aspiration is defined as the period of glottal friction occurring in sequences of vocalic/consonantal sonorants and phonetically voiceless obstruents. We propose two machine learning methods for automatic measurement of pre-aspiration duration: a feedforward neural network, which works at the frame level; and a structured prediction model, which relies on manually designed feature functions, and works at the segment level. The input for both algorithms is a speech signal of an arbitrary length containing a single obstruent, and the output is a pair of times which constitutes the pre-aspiration boundaries. We train both models on a set of manually annotated examples. Results suggest that the structured model is superior to the frame-based model as it yields higher accuracy in predicting the boundaries and generalizes to new speakers and new languages. Finally, we demonstrate the applicability of our structured prediction algorithm by replicating linguistic analysis of pre-aspiration in Aberystwyth English with high correlation

Voice Onset Time Enhanced User System (VOTEUS): a web graphic interface for the analysis of plosives’ release phases

The paper proposes an up-to-date literature review of the works using AutoVOT, a discriminative large-margin learning algorithm developed for the semi-automatic measurement of voice onset times. In order to expand the accessibility of the tool in linguistic research, we present VOTEUS, a user-friendly graphic interface written in Python. The interface is conceived to assist the researcher throughout the whole process of annotation, from the forced alignment of the corpora to the refinement of the AutoVOT tier and the extraction of the durations. The general aim is to speed up this phase of data analysis, providing a significant improvement on prevalent practice to date