Advertisement

A Simple But Effective Approach to Speaker Tracking in Broadcast News

  • Luis Javier Rodríguez
  • Mikel Peñagarikano
  • Germán Bordel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4478)

Abstract

The automatic transcription of broadcast news and meetings involves the segmentation, identification and tracking of speaker turns during each session, which is known as speaker diarization. This paper presents a simple but effective approach to a slightly different task, called speaker tracking, also involving audio segmentation and speaker identification, but with a subset of known speakers, which allows to estimate speaker models and to perform identification on a segment-by-segment basis. The proposed algorithm segments the audio signal in a fully unsupervised way, by locating the most likely change points from an purely acoustic point of view. Then the available speaker data are used to estimate single-Gaussian acoustic models. Finally, speaker models are used to classify the audio segments by choosing the most likely speaker or, alternatively, the Other category, if none of the speakers is likely enough. Despite its simplicity, the proposed approach yielded the best performance in the speaker tracking challenge organized in November 2006 by the Spanish Network on Speech Technology.

Keywords

Audio Stream Broadcast News Speech Database Target Speaker Speaker Model 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tranter, S.E., Reynolds, D.A.: Speaker Diarisation for Broadcast News. In: Proceedings of the ISCA Speaker and Language Recognition Workshop (Odyssey 2004), pp. 337–344. Toledo, Spain. May 31 - June 3 (2004)Google Scholar
  2. 2.
    Jin, Q., Laskowsky, K., Schultz, T., Waibel, A.: Speaker Segmentation and Clustering in Meetings. In: Proceedings of Interspeech 2004 (International Conference on Spoken Language Processing, ICSLP), Jeju Island, South Korea, October 2004, pp. 597–600 (2004)Google Scholar
  3. 3.
    Gauvain, J.L., Lamel, L., Adda, G.: Partitioning and Transcription of Broadcast News Data. In: Proceedings of the International Conference on Spoken Language Processing (ICSLP’98), Sydney, Australia, November-December 1998, pp. 1335–1338 (1998)Google Scholar
  4. 4.
    Chen, S.S., Gopalakrishnan, P.S.: Speaker, Environment and Channel Change Detection and Clustering via the Bayesian Information Criterion. In: Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, Virginia, USA, February 8-11 (1998)Google Scholar
  5. 5.
    Delacourt, P., Wellekens, C.J.: DISTBIC: A speaker-based segmentation for audio data indexing. Speech Communication 32, 111–126 (2000)CrossRefGoogle Scholar
  6. 6.
    Zhou, B., Hansen, J.H.L.: Efficient Audio Stream Segmentation via the Combined T 2 Statistic and Bayesian Information Criterion. IEEE Transactions on Speech and Audio Processing 13(4), 467–474 (2005)CrossRefGoogle Scholar
  7. 7.
    Gish, H., Siu, M.H., Rohlicek, R.: Segregation of Speakers for Speech Recognition and Speaker Identification. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 1991), Toronto, Canada, May 14-17, 1991, pp. 873–876 (1991)Google Scholar
  8. 8.
    Anguera, X., Hernando, J., Anguita, J.: XBIC: nueva medida para segmentación de locutor hacia el indexado automático de la señal de voz. In: Actas de las Terceras Jornadas en Tecnología del Habla, Valencia, España, 17-19 de noviembre 2004, pp. 17–19 (2004)Google Scholar
  9. 9.
    Juang, B.H., Rabiner, L.R.: A Probabilistic Distance Measure for Hidden Markov Models. AT&T Technical Journal 64(2), 391–408 (1985)MathSciNetGoogle Scholar
  10. 10.
    Reynolds, D.A., Rose, R.C.: Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models. IEEE Transactions on Speech and Audio Processing 3(1), 72–83 (1995)CrossRefGoogle Scholar
  11. 11.
    Red Temática de Tecnologías del Habla: Propuesta de Evaluación de Sistemas ALBAYZIN-06 (Segmentación e Identificación de hablantes). IV Jornadas en Tecnología del Habla. Zaragoza, de Noviembre 2006, pp. 8–10 (2006), http://jth2006.unizar.es/evaluacion/albayzin06.html
  12. 12.
    NIST: Spring 2006 (RT-06S) Rich Transcription Meeting Recognition Evaluation Plan, http://www.nist.gov/speech/tests/rt/rt2006/spring/
  13. 13.
    Dunn, R.B., Reynolds, D.A., Quatieri, T.F.: Approaches to Speaker Detection and Tracking in Conversational Speech. Digital Signal Processing 10, 93–112 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Luis Javier Rodríguez
    • 1
  • Mikel Peñagarikano
    • 1
  • Germán Bordel
    • 1
  1. 1.Grupo de Trabajo en Tecnologías del Software, Departamento de Electricidad y Electrónica. Facultad de Ciencia y Tecnología., Universidad del País Vasco. Barrio Sarriena s/n. 48940 LeioaSpain

Personalised recommendations