Skip to content


  • Research Article
  • Open Access

Speech/Nonspeech Detection Using Minimal Walsh Basis Functions

EURASIP Journal on Audio, Speech, and Music Processing20062007:039546

  • Received: 1 November 2005
  • Accepted: 12 June 2006
  • Published:


This paper presents a new method to detect speech/nonspeech components of a given noisy signal. Employing the combination of binary Walsh basis functions and an analysis-synthesis scheme, the original noisy speech signal is modified first. From the modified signals, the speech components are distinguished from the nonspeech components by using a simple decision scheme. Minimal number of Walsh basis functions to be applied is determined using singular value decomposition (SVD). The main advantages of the proposed method are low computational complexity, less parameters to be adjusted, and simple implementation. It is observed that the use of Walsh basis functions makes the proposed algorithm efficiently applicable in real-world situations where processing time is crucial. Simulation results indicate that the proposed algorithm achieves high-speech and nonspeech detection rates while maintaining a low error rate for different noisy conditions.


  • Detection Rate
  • Acoustics
  • Singular Value Decomposition
  • Speech Signal
  • Decision Scheme


Authors’ Affiliations

School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore


  1. ITU-T Recommendation G.729 Annex B : A silence compression scheme for G.729 optimized for terminals conforming to recommendation v.70. 1996Google Scholar
  2. Beritelli F, Casale S, Cavallaro A: A robust voice activity detector for wireless communications using soft computing. IEEE Journal on Selected Areas in Communications 1998,16(9):1818-1829. 10.1109/49.737650View ArticleGoogle Scholar
  3. ETSI GSM 06.94, "Digital cellular telecommunications system (phase 2+); voice activity detectors (VAD) for adaptive multi-rate (AMR) speech traffic channels; european telecommunications standards institute," 1999Google Scholar
  4. McKinley BL, Whipple GH: Model based speech pause detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '97), April 1997, Munich, Germany 2: 1179-1182.Google Scholar
  5. Sohn J, Kim NS, Song W: A statistical model-based voice activity detection. IEEE Signal Processing Letters 1999,6(1):1-3. 10.1109/97.736233View ArticleGoogle Scholar
  6. Cho YD, Kondoz A: Analysis and improvement of a statistical model-based voice activity detector. IEEE Signal Processing Letters 2001,8(10):276-278. 10.1109/97.957270View ArticleGoogle Scholar
  7. Gazor S, Zhang W: A soft voice activity detector based on a Laplacian-Gaussian model. IEEE Transactions on Speech and Audio Processing 2003,11(5):498-505. 10.1109/TSA.2003.815518View ArticleGoogle Scholar
  8. Marzinzik M, Kollmeier B: Speech pause detection for noise spectrum estimation by tracking power envelope dynamics. IEEE Transactions on Speech and Audio Processing 2002,10(2):109-118. 10.1109/89.985548View ArticleGoogle Scholar
  9. Sheikhzadeh H, Brennan RL, Sameti H: Real-time implementation of HMM-based MMSE algorithm for speech enhancement in hearing aid applications. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '95), May 1995, Detroit, Mich, USA 1: 808-811.Google Scholar
  10. Rezayee A, Gazor S: An adaptive KLT approach for speech enhancement. IEEE Transactions on Speech and Audio Processing 2001,9(2):87-95. 10.1109/89.902276View ArticleGoogle Scholar
  11. Wei J, Du L, Yan Z, Zeng H: A new algorithm for voice activity detection. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '03), May 2003, Bangkok, Thailand 2: 588-591.Google Scholar
  12. Jelinek M, Labonté F: Robust signal/noise discrimination for wideband speech and audio coding. Proceedings of the IEEE Workshop on Speech Coding, September 2000, Delavan, Wis, USA 151-153.Google Scholar
  13. Srinivasan K, Gersho A: Voice activity detection for cellular networks. Proceedings of the IEEE Workshop on Speech Coding for Telecommunications, October 1993, Sainte-Adele, Quebec, Canada 85-86.View ArticleGoogle Scholar
  14. Freeman DK, Cosier G, Southcott CB, Boyd I: The voice activity detector for the Pan-European digital cellular mobile telephone service. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '89), May 1989, Glasgow, Scotland, UK 1: 369-372.Google Scholar
  15. Tanyer SG, Özer H: Voice activity detection in nonstationary noise. IEEE Transactions on Speech and Audio Processing 2000,8(4):478-482. 10.1109/89.848229View ArticleGoogle Scholar
  16. Wu Y, Li Y: Robust speech/non-speech detection in adverse conditions using the fuzzy polarity correlation method. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '04), October 2000, The Hague, The Netherlands 4: 2935-2939.View ArticleGoogle Scholar
  17. Quddus A, Gabbouj M: Wavelet-based corner detection technique using optimal scale. Pattern Recognition Letters 2002,23(1–3):215-220.View ArticleMATHGoogle Scholar
  18. Arfib D, Keiler F, Zölzer U: DAFX - Digital Audio Effects. John Wiley & Sons, New York, NY, USA; 2002.Google Scholar
  19. Adjouadi M, Candocia F, Riley J: Exploiting Walsh-based attributes to stereo vision. IEEE Transactions on Signal Processing 1996,44(2):409-420. 10.1109/78.485936View ArticleGoogle Scholar


© M. Pwint and F. Sattar. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.