  We present a novel neural encoder system for acoustic-to-articulatory
inversion. We leverage the Pink Trombone voice synthesizer that reveals
articulatory parameters (e.g tongue position and vocal cord configuration). Our
system is designed to identify the articulatory features responsible for
producing specific acoustic characteristics contained in a neural latent
representation. To generate the necessary latent embeddings, we employed two
main methodologies. The first was a self-supervised variational autoencoder
trained from scratch to reconstruct the input signal at the decoder stage. We
conditioned its bottleneck layer with a subnetwork called the "projector,"
which decodes the voice synthesizer's parameters.
  The second methodology utilized two pretrained models: EnCodec and Wav2Vec.
They eliminate the need to train the encoding process from scratch, allowing us
to focus on training the projector network. This approach aimed to explore the
potential of these existing models in the context of acoustic-to-articulatory
inversion. By reusing the pretrained models, we significantly simplified the
data processing pipeline, increasing efficiency and reducing computational
overhead.
  The primary goal of our project was to demonstrate that these neural
architectures can effectively encapsulate both acoustic and articulatory
features. This prediction-based approach is much faster than traditional
methods focused on acoustic feature-based parameter optimization. We validated
our models by predicting six different parameters and evaluating them with
objective and ViSQOL subjective-equivalent metric using both synthesizer- and
human-generated sounds. The results show that the predicted parameters can
generate human-like vowel sounds when input into the synthesizer. We provide
the dataset, code, and detailed findings to support future research in this
field.
