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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)

Paper ID
12603
Venue
VLDB
Year
2021
Pagerank
5.093636e-05
Overall Rank
11,704 | 19.70%
DOI
10.14778/3476249.3476289

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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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