skip to main content
10.1145/2854946.2854987acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
short-paper

Vapor Engine: Demonstrating an Early Prototype of a Language-Independent Search Engine for Speech

Published: 13 March 2016 Publication History

Abstract

Typical search engines for spoken content begin with some form of language-specific audio processing such as phonetic word recognition. Many languages, however, lack the language tuned preprocessing tools that are needed to create indexing terms for speech. One approach in such cases is to rely on repetition, detected using acoustic features, to find terms that might be worth indexing. Experiments have shown that this approach yields term sets that might be sufficient for some applications in both spoken term detection and ranked retrieval experiments. Such approaches currently work only with spoken queries, however, and only when the searcher is able to speak in a manner similar to that of the speakers in the collection. This demonstration paper proposes Vapor Engine, a new tool for selectively transcribing repeated terms that can be automatically detected from spoken content in any language. These transcribed terms could then be matched to queries formulated using written terms. Vapor Engine is early in development: it currently supports only single-term queries and has not yet having been formally evaluated. This paper introduces the interface and summarizes the challenges it seeks to address.

References

[1]
F. Abdulhamid and S. Marshall. Treemaps to visualise and navigate speech audio. In OZCHI, 2013.
[2]
L. Begeja et al. A system for searching and browsing spoken communications. In NAACL-HLT, 2004.
[3]
J. Cui et al. Easyalbum: an interactive photo annotation system based on face clustering and re-ranking. In CHI, 2007.
[4]
M. Dredze et al. NLP on spoken documents without ASR. In EMNLP, 2010.
[5]
J. Goldstein et al. Annotating subsets of the enron email corpus. In CEAS, 2006.
[6]
A. Jansen and B. Van Durme. Efficient spoken term discovery using randomized algorithms. In ASRU, 2011.
[7]
S. Luz et al. Supporting collaborative transcription of recorded speech with a 3D game interface. In KES, 2010.
[8]
M. Marge et al. Using the Amazon Mechanical Turk to transcribe and annotate meeting speech for extractive summarization. In NAACL-HLT, 2010.
[9]
A. Muscariello et al. Unsupervised motif acquisition in speech via seeded discovery and template matching combination. In ICASSP, 2012.
[10]
K. Ng. Subword-Based Approaches for Spoken Document Retrieval. PhD thesis, MIT, 1990.
[11]
S. Novotney and C. Callison-Burch. Cheap, fast and good enough: Automatic speech recognition with non-expert transcription. In NAACL-HLT, 2010.
[12]
D. Oard et al. The FIRE 2013 question answering for the spoken web task. In FIRE, 2013.
[13]
A. Park and J. Glass. Unsupervised pattern discovery in speech. In ICASSP, 2008.
[14]
M. A. Pitt et al. The Buckeye corpus of conversational speech: Labeling conventions and a test of transcriber reliability. Speech Communication, 2005.
[15]
A. Ranjan et al. Searching in audio: the utility of transcripts, dichotic presentation, and time-compression. In CHI, 2006.
[16]
J. White et al. Using zero-resource spoken term discovery for ranked retrieval. In NAACL-HLT, 2015.
[17]
S. Whittaker et al. SCAN: Designing and evaluating user interfaces to support retrieval from speech archives. In SIGIR, 1999.
[18]
Y. Zhang and J. Glass. Towards multi-speaker unsupervised speech pattern discovery. In ICASSP, 2010.

Cited By

View all
  • (2017)Simulating Zero-Resource Spoken Term DiscoveryProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133160(2371-2374)Online publication date: 6-Nov-2017

Index Terms

  1. Vapor Engine: Demonstrating an Early Prototype of a Language-Independent Search Engine for Speech

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CHIIR '16: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval
    March 2016
    400 pages
    ISBN:9781450337519
    DOI:10.1145/2854946
    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 the author(s) 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

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 March 2016

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. demonstration
    2. speech retrieval

    Qualifiers

    • Short-paper

    Funding Sources

    • National Science Foundation

    Conference

    CHIIR '16
    Sponsor:
    CHIIR '16: Conference on Human Information Interaction and Retrieval
    March 13 - 17, 2016
    North Carolina, Carrboro, USA

    Acceptance Rates

    CHIIR '16 Paper Acceptance Rate 23 of 58 submissions, 40%;
    Overall Acceptance Rate 55 of 163 submissions, 34%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 25 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2017)Simulating Zero-Resource Spoken Term DiscoveryProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133160(2371-2374)Online publication date: 6-Nov-2017

    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