- Research Article
- Open Access
Speech/Nonspeech Detection Using Minimal Walsh Basis Functions
EURASIP Journal on Audio, Speech, and Music Processing volume 2007, Article number: 039546 (2006)
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.
ITU-T Recommendation G.729 Annex B : A silence compression scheme for G.729 optimized for terminals conforming to recommendation v.70. 1996
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. 10.1109/49.737650
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," 1999
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.
Sohn J, Kim NS, Song W: A statistical model-based voice activity detection. IEEE Signal Processing Letters 1999,6(1):1-3. 10.1109/97.736233
Cho YD, Kondoz A: Analysis and improvement of a statistical model-based voice activity detector. IEEE Signal Processing Letters 2001,8(10):276-278. 10.1109/97.957270
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.2003.815518
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. 10.1109/89.985548
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.
Rezayee A, Gazor S: An adaptive KLT approach for speech enhancement. IEEE Transactions on Speech and Audio Processing 2001,9(2):87-95. 10.1109/89.902276
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.
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.
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.
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.
Tanyer SG, Özer H: Voice activity detection in nonstationary noise. IEEE Transactions on Speech and Audio Processing 2000,8(4):478-482. 10.1109/89.848229
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.
Quddus A, Gabbouj M: Wavelet-based corner detection technique using optimal scale. Pattern Recognition Letters 2002,23(1–3):215-220.
Arfib D, Keiler F, Zölzer U: DAFX - Digital Audio Effects. John Wiley & Sons, New York, NY, USA; 2002.
Adjouadi M, Candocia F, Riley J: Exploiting Walsh-based attributes to stereo vision. IEEE Transactions on Signal Processing 1996,44(2):409-420. 10.1109/78.485936
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Pwint, M., Sattar, F. Speech/Nonspeech Detection Using Minimal Walsh Basis Functions. J AUDIO SPEECH MUSIC PROC. 2007, 039546 (2006). https://doi.org/10.1155/2007/39546
- Detection Rate
- Singular Value Decomposition
- Speech Signal
- Decision Scheme