  In this paper, we propose a neural articulation-to-speech (ATS) framework
that synthesizes high-quality speech from articulatory signal in a
multi-speaker situation. Most conventional ATS approaches only focus on
modeling contextual information of speech from a single speaker's articulatory
features. To explicitly represent each speaker's speaking style as well as the
contextual information, our proposed model estimates style embeddings, guided
from the essential speech style attributes such as pitch and energy. We adopt
convolutional layers and transformer-based attention layers for our model to
fully utilize both local and global information of articulatory signals,
measured by electromagnetic articulography (EMA). Our model significantly
improves the quality of synthesized speech compared to the baseline in terms of
objective and subjective measurements in the Haskins dataset.
