  Synthesized speech from articulatory movements can have real-world use for
patients with vocal cord disorders, situations requiring silent speech, or in
high-noise environments. In this work, we present EMA2S, an end-to-end
multimodal articulatory-to-speech system that directly converts articulatory
movements to speech signals. We use a neural-network-based vocoder combined
with multimodal joint-training, incorporating spectrogram, mel-spectrogram, and
deep features. The experimental results confirm that the multimodal approach of
EMA2S outperforms the baseline system in terms of both objective evaluation and
subjective evaluation metrics. Moreover, results demonstrate that joint
mel-spectrogram and deep feature loss training can effectively improve system
performance.
