  Most of the speech recognition systems recover only words in the speech and
fail to capture emotions. Users have to manually add emoji(s) in text for
adding tone and making communication fun. Though there is much work done on
punctuation addition on transcribed speech, the area of emotion addition is
untouched. In this paper, we propose a novel on-device pipeline to enrich the
voice input experience. It involves, given a blob of transcribed text,
intelligently processing and identifying structure where emoji insertion makes
sense. Moreover, it includes semantic text analysis to predict emoji for each
of the sub-parts for which we propose a novel architecture Attention-based Char
Aware (ACA) LSTM which handles Out-Of-Vocabulary (OOV) words as well. All these
tasks are executed completely on-device and hence can aid on-device dictation
systems. To the best of our knowledge, this is the first work that shows how to
add emoji(s) in the transcribed text. We demonstrate that our components
achieve comparable results to previous neural approaches for punctuation
addition and emoji prediction with 80% fewer parameters. Overall, our proposed
model has a very small memory footprint of a mere 4MB to suit on-device
deployment.
