Demonstrating Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries
Summary: MUVE uses multiplots to visualize phonetically similar voice-to-SQL queries, mapping input to candidates and selecting subset to reduce ASR risk. NP-hard display optimization; IP-based exhaustive search and a greedy heuristic yield faster results. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Ziyun Wei (Cornell University)
- 2. Immanuel Trummer (Cornell University)
- 3. Connor Anderson (Cornell University)
BibTeX Citation
@inproceedings{wei_sigmod21,
title = {{Demonstrating Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries}},
author = {Wei, Ziyun and Trummer, Immanuel and Anderson, Connor},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452753},
url = {https://dl.acm.org/doi/10.1145/3448016.3452753},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,058 | Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries | 2021 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 430 | ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores | 2016 | VLDB | 0.00018415668 |
| 523 | NaLIR: An Interactive Natural Language Interface for Querying Relational Databases | 2014 | SIGMOD | 0.0001689006 |
| 1,887 | Making the Case for Query-by-Voice with EchoQuery | 2016 | SIGMOD | 9.4182022e-05 |
| 4,710 | Demonstration of SpeakQL: Speech-driven Multimodal Querying of Structured Data | 2019 | SIGMOD | 6.4410629e-05 |
| 5,058 | Mining Precision Interfaces From Query Logs | 2019 | SIGMOD | 6.2817535e-05 |
| 5,589 | SpeakQL: Towards Speech-driven Multimodal Querying | 2019 | SIGMOD | 6.0560187e-05 |
| 5,877 | SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data | 2020 | SIGMOD | 5.9450095e-05 |
| 6,673 | Natural Language Querying of Complex Business Intelligence Queries | 2019 | SIGMOD | 5.697263e-05 |
| 7,129 | Data Vocalization with CiceroDB | 2019 | CIDR | 5.580601e-05 |
| 12,191 | A Holistic Approach for Query Evaluation and Result Vocalization in Voice-Based OLAP | 2019 | SIGMOD | 4.9605343e-05 |
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| 2 | 12,280 | Vocalizing Large Time Series Efficiently | 2018 | VLDB |
| 3 | 12,191 | A Holistic Approach for Query Evaluation and Result Vocalization in Voice-Based OLAP | 2019 | SIGMOD |
| 4 | 5,589 | SpeakQL: Towards Speech-driven Multimodal Querying | 2019 | SIGMOD |
| 5 | 5,877 | SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data | 2020 | SIGMOD |
| 6 | 11,902 | VoiceQuerySystem: A Voice-driven Database Querying System Using Natural Language Questions | 2022 | SIGMOD |
| 7 | 4,710 | Demonstration of SpeakQL: Speech-driven Multimodal Querying of Structured Data | 2019 | SIGMOD |
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