skip to main content
10.1145/1900008.1900095acmconferencesArticle/Chapter ViewAbstractPublication Pagesacm-seConference Proceedingsconference-collections
research-article

Supporting license plate queries for first responders using the voiceLETS system

Published: 15 April 2010 Publication History

Abstract

The need for delivering quick and accurate information to first responders, such as law enforcement officers, is important for providing them with the resources needed to do their jobs safely and effectively. The common method of information exchange from officers to emergency dispatchers is problematic in that response time and communicative consistency can result in inaccurate or untimely information. Although information requests by officers currently require the use of defined alpha codes to ensure the accuracy of vehicle license plate sequences, the proper use is inconsistent. We introduce in this paper an adaption of VoiceLETS, [1] which provides an algorithm to detect and predict license sequences without the use of alpha codes. Preliminary testing of this algorithm showed a 34.2% increase in the accuracy of tag query results. There was also a correction accuracy of 95.35% when the system attempted to correct misinterpreted characters within a query.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
ACMSE '10, April 15--17, 2010, Oxford, MS, USA

References

[1]
CRDL, "Law Enforcement Tactical System (LETS)"; 16 Mar. 2004; care.cs.ua.edu/lets.aspx
[2]
Juan E. Gilbert, Richard Chapman, Sangeeta Garhyan, "VoiceLETS Backs Up First Responders," IEEE Pervasive Computing, vol. 4, no. 3, pp. 92--96, July-September, 2
[3]
M. Handley et al., "SIP: Session Initiative Protocol," Internet Eng. Task Force, 1999; www.ietf.org/rfc/rfc2543
[4]
Zhong, Y. & Gilbert, J. E. (2005) A Context-Aware Language Model for Spoken Query Retrieval. International Journal of Speech Technology, 8, 2, Springer, pp. 203--219.
[5]
W3C, " Voice Extensible Markup Language (VoiceXML) Version 2.0". W3C Recommendation, 16 Mar. 2004; www.w3.org/TR/2004/RECvoicesml20-20040316

Index Terms

  1. Supporting license plate queries for first responders using the voiceLETS system

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    ACMSE '10: Proceedings of the 48th annual ACM Southeast Conference
    April 2010
    488 pages
    ISBN:9781450300643
    DOI:10.1145/1900008
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 April 2010

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. VoiceLETS
    2. alphabet recognition algorithm
    3. first responders
    4. license tag search
    5. mobile communication
    6. public safety
    7. query dialogue
    8. speech recognition
    9. voice-based user interfaces

    Qualifiers

    • Research-article

    Funding Sources

    Conference

    ACM SE '10
    Sponsor:
    ACM SE '10: ACM Southeast Regional Conference
    April 15 - 17, 2010
    Mississippi, Oxford

    Acceptance Rates

    ACMSE '10 Paper Acceptance Rate 48 of 94 submissions, 51%;
    Overall Acceptance Rate 502 of 1,023 submissions, 49%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 89
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 18 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media