  Person name capture from human speech is a difficult task in human-machine
conversations. In this paper, we propose a novel approach to capture the person
names from the caller utterances in response to the prompt "say and spell your
first/last name". Inspired from work on spell correction, disfluency removal
and text normalization, we propose a lightweight Seq-2-Seq system which
generates a name spell from a varying user input. Our proposed method
outperforms the strong baseline which is based on LM-driven rule-based
approach.
