Spoken Term Detection System Based on Combination of LVCSR and Phonetic Search

  • Igor Szöke
  • Michal Fapšo
  • Martin Karafiát
  • Lukáš Burget
  • František Grézl
  • Petr Schwarz
  • Ondřej Glembek
  • Pavel Matějka
  • Jiří Kopecký
  • Jan “Honza” Černocký
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4892)

Abstract

The paper presents the Brno University of Technology (BUT) system for indexing and search of speech, combining LVCSR and phonetic approach. It brings a complete description of individual building blocks of the system from signal processing, through the recognizers, indexing and search until the normalization of detection scores. It also describes the data used in the first edition of NIST Spoken term detection (STD) evaluation. The results are presented on three US-English conditions - meetings, broadcast news and conversational telephone speech, in terms of detection error trade-off (DET) curves and term-weighted values (TWV) metrics defined by NIST.

Keywords

Language Model Acoustic Model Inverted Index Broadcast News Maximum Likelihood Linear Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Burget, L., Černocký, J., Fapšo, M., Karafiát, M., Matějka, P., Schwarz, P., Smrž, P., Szöke, I.: Indexing and search methods for spoken documents. In: TSD 2006, pp. 351–358 (September 2006)Google Scholar
  2. 2.
    NIST Spoken Term Detection Evaluation, http://www.nist.gov/speech/tests/std/
  3. 3.
    van Leeuwen, D.A., Huijbregts, M.: The AMI Speaker Diarization System for NIST RT06s Meeting Data. In: Renals, S., Bengio, S., Fiscus, J.G. (eds.) MLMI 2006. LNCS, vol. 4299, pp. 371–384. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Hain, T., et al.: The AMI Meeting Transcription System. In: Proc. NIST Rich Transcription 2006 Spring Meeting Recognition Evaluation Worskhop, p. 12. Washington D.C., USA (2006)Google Scholar
  5. 5.
    Grézl, F., Karafiát, M., Kontár, S., Černocký, J.: Probabilistic and bottle-neck features for LVCSR of meetings. In: Proc. ICASSP 2007, Hawaii (2007)Google Scholar
  6. 6.
    Schwarz, P., Matějka, P., Černocký, J.: Towards Lower Error Rates in Phoneme Recognition. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Eastern European Speech Databases for Creation of Voice Driven Teleservices, http://www.fee.vutbr.cz/SPEECHDAT-E/
  8. 8.
    Povey, D.: Discriminative Training for Large Vocabulary Speech, Recognition, PhD. Thesis, Cambridge University (July 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Igor Szöke
    • 1
  • Michal Fapšo
    • 1
  • Martin Karafiát
    • 1
  • Lukáš Burget
    • 1
  • František Grézl
    • 1
  • Petr Schwarz
    • 1
  • Ondřej Glembek
    • 1
  • Pavel Matějka
    • 1
  • Jiří Kopecký
    • 1
  • Jan “Honza” Černocký
    • 1
  1. 1.Speech@FIT, Faculty of Information Technology, Brno University of Technology, Email: speech@fit.vutbr.cz 

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