Several technologies, such as electromagnetic midsagittal articulography
(EMA) or real-time magnetic resonance (RT-MRI), enable studying the
static and dynamic aspects of speech production. The resulting knowledge
can, in turn, inform the improvement of speech production models, e.g.,
for articulatory speech synthesis, by enabling the identification of
which articulators and gestures are involved in producing specific
sounds.
The amount of data available from these technologies, and the
need for a systematic quantitative assessment, advise tackling these
matters through data-driven approaches, preferably unsupervised, since
annotated data is scarce. In this context, a method for statistical
identification of critical articulators has been proposed, in the literature,
and successfully applied to EMA data. However, the many differences
regarding the data available from other technologies, such as RT-MRI,
and language-specific aspects create a challenging setting for its
direct and wider applicability.
In this article, we
address the steps needed to extend the applicability of the proposed
statistical analyses, initially applied to EMA, to an existing RT-MRI
corpus and test it for a different language, European Portuguese. The
obtained results, for three speakers, and considering 33 phonemes,
provide phonologically meaningful critical articulator outcomes and
show evidence of the applicability of the method to RT-MRI.
Cite as: Silva, S., Teixeira, A. (2017) Critical Articulators Identification from RT-MRI of the Vocal Tract. Proc. Interspeech 2017, 626-630, doi: 10.21437/Interspeech.2017-742
@inproceedings{silva17_interspeech,
author={Samuel Silva and António Teixeira},
title={{Critical Articulators Identification from RT-MRI of the Vocal Tract}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={626--630},
doi={10.21437/Interspeech.2017-742}
}