Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries
Summary: MUVE visualizes phonetically similar voice-to-SQL results as multiplots to curb ASR ambiguity. It maps voice input to a candidate distribution, selects a subset of queries, and optimizes visualization to minimize the expected time to identify the correct result; NP-hard, with IP-based exact and greedy solvers, validated by a user study. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ziyun Wei (Cornell University)
- 2. Immanuel Trummer (Cornell University)
- 3. Connor Anderson (Cornell University)
BibTeX Citation
@article{wei_vldb21,
title = {{Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries}},
author = {Wei, Ziyun and Trummer, Immanuel and Anderson, Connor},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2397--2409},
doi = {10.14778/3476249.3476289},
url = {https://doi.org/10.14778/3476249.3476289},
year = {2021}
}
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