Database Paper Browser

Back to papers

Sevi: Speech-to-Visualization through Neural Machine Translation

Summary: Sevi is an end-to-end Speech-to-Visualization system mapping natural language or speech to visualizations via ncNet, a neural MT model trained on nvBench. It couples Speech2Text with Text2VIS to enable cross-domain visualization synthesis, demonstrated on COVID-19 and NBA datasets across nvBench’s 105 domains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6366
Venue
SIGMOD
Year
2022
Pagerank
4.2751057e-05
Overall Rank
9,829 | 31.63%
DOI
10.1145/3514221.3520150

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers