Evaluating Features and Metrics for High-Quality Simulation of Early Vocal Learning of Vowels
Abstract
The way infants use auditory cues to learn to speak despite the acoustic mismatch of their vocal apparatus is a hot topic of scientific debate. The simulation of early vocal learning using articulatory speech synthesis offers a way towards gaining a deeper understanding of this process. One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target. We contribute with evaluating the performance of a set of 40 feature-metric combinations for the task of optimising the production of static vowels with a high-quality articulatory synthesiser. Towards this end we assess the usability of formant error and the projection of the feature-metric error surface in the normalised F1-F2 formant space. We show that this approach can be used to evaluate the impact of features and metrics and also to offer insight to perceptual results.
Keywords
Cite
@article{arxiv.2005.09986,
title = {Evaluating Features and Metrics for High-Quality Simulation of Early Vocal Learning of Vowels},
author = {Branislav Gerazov and Daniel van Niekerk and Anqi Xu and Paul Konstantin Krug and Peter Birkholz and Yi Xu},
journal= {arXiv preprint arXiv:2005.09986},
year = {2021}
}
Comments
Submitted to INTERSPEECH 2021