  We investigate multi-speaker speech recognition from ultrasound images of the
tongue and video images of the lips. We train our systems on imaging data from
modal speech, and evaluate on matched test sets of two speaking modes: silent
and modal speech. We observe that silent speech recognition from imaging data
underperforms compared to modal speech recognition, likely due to a
speaking-mode mismatch between training and testing. We improve silent speech
recognition performance using techniques that address the domain mismatch, such
as fMLLR and unsupervised model adaptation. We also analyse the properties of
silent and modal speech in terms of utterance duration and the size of the
articulatory space. To estimate the articulatory space, we compute the convex
hull of tongue splines, extracted from ultrasound tongue images. Overall, we
observe that the duration of silent speech is longer than that of modal speech,
and that silent speech covers a smaller articulatory space than modal speech.
Although these two properties are statistically significant across speaking
modes, they do not directly correlate with word error rates from speech
recognition.
