Simulating Articulatory Trajectories with Phonological Feature Interpolation

As a first step towards a complete computational model of speech learning involving perception-production loops, we investigate the forward mapping between pseudo-motor commands and articulatory trajectories. Two phonological feature sets, based respectively on generative and articulatory phonology,...

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Bibliographic Details
Published inarXiv.org
Main Authors Angelo Ortiz Tandazo, Schatz, Thomas, Hueber, Thomas, Dupoux, Emmanuel
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.08.2024
Subjects
Articulation (speech)
Biological effects
Interpolation
Trajectory optimization
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Summary:As a first step towards a complete computational model of speech learning involving perception-production loops, we investigate the forward mapping between pseudo-motor commands and articulatory trajectories. Two phonological feature sets, based respectively on generative and articulatory phonology, are used to encode a phonetic target sequence. Different interpolation techniques are compared to generate smooth trajectories in these feature spaces, with a potential optimisation of the target value and timing to capture co-articulation effects. We report the Pearson correlation between a linear projection of the generated trajectories and articulatory data derived from a multi-speaker dataset of electromagnetic articulography (EMA) recordings. A correlation of 0.67 is obtained with an extended feature set based on generative phonology and a linear interpolation technique. We discuss the implications of our results for our understanding of the dynamics of biological motion.
ISSN:2331-8422