  We aim to solve the highly challenging task of generating continuous sign
language videos solely from speech segments for the first time. Recent efforts
in this space have focused on generating such videos from human-annotated text
transcripts without considering other modalities. However, replacing speech
with sign language proves to be a practical solution while communicating with
people suffering from hearing loss. Therefore, we eliminate the need of using
text as input and design techniques that work for more natural, continuous,
freely uttered speech covering an extensive vocabulary. Since the current
datasets are inadequate for generating sign language directly from speech, we
collect and release the first Indian sign language dataset comprising
speech-level annotations, text transcripts, and the corresponding sign-language
videos. Next, we propose a multi-tasking transformer network trained to
generate signer's poses from speech segments. With speech-to-text as an
auxiliary task and an additional cross-modal discriminator, our model learns to
generate continuous sign pose sequences in an end-to-end manner. Extensive
experiments and comparisons with other baselines demonstrate the effectiveness
of our approach. We also conduct additional ablation studies to analyze the
effect of different modules of our network. A demo video containing several
results is attached to the supplementary material.
