Angelo Ortiz Tandazo

Centre Sciences des Données

45, rue d'Ulm

75005 Paris, France

I am a PhD student at the Cognitive ML team (LSCP, ENS) and the CRISSP team (GIPSA-lab, Grenoble-INP) working on modelling early vocal learning with a simulated vocal tract under the supervision of Emmanuel Dupoux and Thomas Hueber.

I hold a BSc in Computer Science from Sorbonne University, and the Diploma in Computer Science from École Normale Supérieure. As part of my programme at ENS, I also obtained an MSc in Machine Learning (Master 2 MVA) from École Normale Supérieure Paris-Saclay.

news

Jun 4, 2024	Our paper Simulating articulatory trajectories with phonological feature interpolation has been accepted at Interspeech 2024.
Oct 2, 2023	I started my PhD on early vocal learning at LSCP and GIPSA-lab.
Apr 11, 2023	I started my internship on the development and implementation of a gestural model for speech perception at Aix-Marseille University.
Oct 10, 2022	I started my internship on gestural speech perception at Cognitive ML.

selected publications

Simulating articulatory trajectories with phonological feature interpolation

Angelo Ortiz Tandazo, Thomas Schatz, Thomas Hueber, and 1 more author

In Interspeech 2024, 2024

Abs Bib PDF Code

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}, }