  In human-computer conversations, extracting entities such as names, street
addresses and email addresses from speech is a challenging task. In this paper,
we study the impact of fine-tuning pre-trained speech encoders on extracting
spoken entities in human-readable form directly from speech without the need
for text transcription. We illustrate that such a direct approach optimizes the
encoder to transcribe only the entity relevant portions of speech ignoring the
superfluous portions such as carrier phrases, or spell name entities. In the
context of dialog from an enterprise virtual agent, we demonstrate that the
1-step approach outperforms the typical 2-step approach which first generates
lexical transcriptions followed by text-based entity extraction for identifying
spoken entities.
