  Silent speech interfaces (SSI) has been an exciting area of recent interest.
In this paper, we present a non-invasive silent speech interface that uses
inaudible acoustic signals to capture people's lip movements when they speak.
We exploit the speaker and microphone of the smartphone to emit signals and
listen to their reflections, respectively. The extracted phase features of
these reflections are fed into the deep learning networks to recognize speech.
And we also propose an end-to-end recognition framework, which combines the CNN
and attention-based encoder-decoder network. Evaluation results on a limited
vocabulary (54 sentences) yield word error rates of 8.4% in speaker-independent
and environment-independent settings, and 8.1% for unseen sentence testing.
