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.
@inproceedings{ortiztandazo24_interspeech,title={Simulating articulatory trajectories with phonological feature interpolation},author={{Ortiz Tandazo}, Angelo and Schatz, Thomas and Hueber, Thomas and Dupoux, Emmanuel},year={2024},booktitle={Interspeech 2024},pages={3595--3599},doi={10.21437/Interspeech.2024-2192},issn={2958-1796},}