# Automatic Measurement of Pre-aspiration

## TL;DR

This paper introduces two machine learning methods for automatically measuring pre-aspiration duration in speech, demonstrating that a structured prediction model outperforms a frame-based neural network in accuracy and generalization.

## Contribution

The paper presents a novel structured prediction approach for automatic pre-aspiration measurement, outperforming a neural network baseline and applicable across languages.

## Key findings

- Structured model yields higher boundary prediction accuracy.
- Structured model generalizes to new speakers and languages.
- High correlation with linguistic analysis in Aberystwyth English.

## Abstract

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.