Simulating Articulatory Trajectories with Phonological Feature Interpolation
<|reference_start|>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, 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.<|reference_end|>
@article{tandazo2024simulating,
    title={Simulating Articulatory Trajectories with Phonological Feature
  Interpolation},
    author={Angelo Ortiz Tandazo, Thomas Schatz, Thomas Hueber, Emmanuel Dupoux},
    journal={arXiv preprint arXiv:2408.04363},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04363},
    primaryClass={eess.AS cs.CL}
}