short-paper

Demonstrating Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries

Authors:

Immanuel Trummer,

Connor AndersonAuthors Info & Claims

SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data

Pages 2798 - 2802

https://doi.org/10.1145/3448016.3452753

Published: 18 June 2021 Publication History

Abstract

Recently proposed voice query interfaces translate voice input into SQL queries. Unreliable speech recognition on top of the intrinsic challenges of text-to-SQL translation makes it hard to reliably interpret user input. We present MUVE (Multiplots for Voice quEries), a system for robust voice querying. MUVE reduces the impact of ambiguous voice queries by filling the screen with multiplots, capturing results of phonetically similar queries. It maps voice input to a probability distribution over query candidates, executes a selected subset of queries, and visualizes their results in a multiplot.

Our goal is to maximize probability to show the correct query result. Also, we want to optimize the visualization (e.g., by coloring a subset of likely results) in order to minimize expected time until users find the correct result. Via a user study, we validate a simple cost model estimating the latter overhead. The resulting optimization problem is NP-hard. We propose an exhaustive algorithm, based on integer programming, as well as a greedy heuristic. As shown in a corresponding user study, MUVE enables users to identify accurate results faster, compared to prior work.

Supplementary Material

MP4 File (3448016.3452753.mp4)

Recently proposed voice query interfaces translate voice input into SQL queries. Unreliable speech recognition on top of the intrinsic challenges of text-to-SQL translation makes it hard to reliably interpret user input. We present MUVE (Multiplots for Voice quEries), a system for robust voice querying. MUVE reduces the impact of ambiguous voice queries by filling the screen with multiplots, capturing results of phonetically similar queries. It maps voice input to a probability distribution over query candidates, executes a selected subset of queries, and visualizes their results in a multiplot. Our goal is to maximize the probability to show the result of the correct query. This is a hard optimization problem. We propose an exhaustive algorithm, based on integer programming, as well as a greedy heuristic. As shown in a corresponding user study, MUVEenables users to identify accurate results faster, compared to prior work. In our demonstration, participants can experiment with both approaches (accessible in an online interface) and try voice queries on different data sets.

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Cited By

Wei ZTrummer IAnderson C(2021)Robust voice querying with MUVEProceedings of the VLDB Endowment10.14778/3476249.347628914:11(2397-2409)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.14778/3476249.3476289
Wong AJoiner DChiu CElsayed MPereira KKhmelevsky YMahony J(2021)A Survey of Natural Language Processing Implementation for Data Query Systems2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)10.1109/RASSE53195.2021.9686815(1-8)Online publication date: 12-Dec-2021
https://doi.org/10.1109/RASSE53195.2021.9686815

Index Terms

Demonstrating Robust Voice Querying with MUVE: Optimally Visualizing Results of Phonetically Similar Queries
1. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods
      1. User interface design
2. Information systems
  1. Data management systems
    1. Database management system engines

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cover image ACM Conferences

SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data

June 2021

2969 pages

ISBN:9781450383431

DOI:10.1145/3448016

General Chairs:
Guoliang Li
Tsinghua University (China)
,
Zhanhuai Li
Northwestern Polytechnical University (China)
,
Program Chairs:
Stratos Idreos
Harvard University (USA)
,
Divesh Srivastava
AT&T (USA)

Copyright © 2021 ACM.

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New York, NY, United States

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Published: 18 June 2021

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SIGMOD/PODS '21: International Conference on Management of Data

June 20 - 25, 2021

Virtual Event, China

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Cited By

Wei ZTrummer IAnderson C(2021)Robust voice querying with MUVEProceedings of the VLDB Endowment10.14778/3476249.347628914:11(2397-2409)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.14778/3476249.3476289
Wong AJoiner DChiu CElsayed MPereira KKhmelevsky YMahony J(2021)A Survey of Natural Language Processing Implementation for Data Query Systems2021 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)10.1109/RASSE53195.2021.9686815(1-8)Online publication date: 12-Dec-2021
https://doi.org/10.1109/RASSE53195.2021.9686815

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