Punctuation restoration research
Improving STT output by restoring punctuation from multimodal signals
Context
A university–industry project on STT output quality, the front end of the counselling analysis pipeline.
Problem
ASR output arrives without punctuation, which hurts readability and drags down every downstream step — summarisation, emotion analysis, and the rest.
How I solved it
- Surveyed prior work and confirmed where text-only restoration breaks down
- Designed and built a multimodal model that infers from text and audio features together
- Validated the PoC through a Streamlit demo
Result
- F1 score improved 15% over text-only punctuation restoration
Impact
Everything downstream of STT — summarisation, emotion analysis — now receives cleaner input.