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Enhancement Voiced/Unvoiced Sounds Classification for 3GPP2 SMV Employing GMM  

Song, Ji-Hyun (Department of Electronics Engineering, Inha University)
Chang, Joon-Hyuk (Department of Electronics Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.45, no.5, 2008 , pp. 111-117 More about this Journal
Abstract
In this paper, we propose an approach to improve the performance of voiced/unvoiced (V/UV) decision under background noise environments for the selectable mode vocoder (SMV) of 3GPP2. We first present an effective analysis of the features and the classification method adopted in the SMV. And then feature vectors which are applied to the GMM are selected from relevant parameters of the SMV for the efficient voiced/unvoiced classification. For the purpose of evaluating the performance of the proposed algorithm, different experiments were carried out under various noise environments and yields better results compared with the conventional scheme of the SMV.
Keywords
Voiced/unvoiced classification; Selectable Mode Vocoder (SMV); Gaussian Mixture Model (GMM);
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1 L. R. Rabiner and M. R. Sambur, "Application of an LPC Distance Measure to the Voiced- Unvoiced -Silence Detection Problem," IEEE Trans. Acoust. Speech Signal Process., vol. ASSP-25, no. 4, pp. 339-343, Aug. 1977
2 P. Vary and R. Martin, Digital Speech Transmission : enhancement, coding and error concealment, pp.182-187, 2006
3 B. Atal and L. R. Rabiner, "A pattern recognition approach to voiced-unvoiced-silence classification with application to speech recognition," IEEE Trans. Acoust. Speech Signal Process., vol. ASSP-24, pp. 201-212, Jun. 1976
4 C. V. Goudar, P. Rabha, M. Deshpande and A. Rao, "SMVLite: Reduced Complexity Selectable Mode Vocoder," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 701-704, May 2006
5 S. Ahmadi and A. S. Spanias, "Cepstrum-based pitch detection using a new statistical V/UV classification algorithm," IEEE Trans. Speech Audio Process., vol. 7, no. 3, May 1999
6 Y. Gao, E. Shlomot, A. Benyassine, J. hyssen, Huan-yu Su, and C. Murgia, "The SMV Algorithm Selected by TIA and 3GPP2 for CDMA Applications," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 709-712, May 2001
7 S. C. Greer, and A. Dejaco, "Standardization of the selectable mode vocoder," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 953-956, May 2001
8 G. Xuan, W. Zhang and P. Chai, "EM algorithm of gaussian mixture model and hidden Markov model," Proc. IEEE International Conference on Image Processing, vol. 1, pp. 145-148, Oct. 2001
9 D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, "Speaker verification using adapted Gaussian mixture models," Digital Signal Processing, vol. 10, pp. 19-41, Jan. 2000   DOI   ScienceOn
10 J. -H. Chang, N. S. Kim, and S. K. Mitra, "A statistical model-based V/UV decision under background noise environments," IEICE Trans. Inf. & Syst., vol. E87-D, no. 12, pp.2885-2887, Dec. 2004
11 L. Siegel, "A procedure for using pattern classification techniques to obtain a voiced/unvoiced/ classifier," IEEE Trans. Acoust. Speech Signal Process., vol. ASSP_27, pp. 83-88, Jun. 1976
12 3GPP2 Spec., "Selectable Mode Vocoder (SMV) Service Option for Wideband Spread Spectrum Communication Systems," 3GPP2- C.S0030-0, v3.0, Jan. 2004
13 3GPP2 Spec., "Source-controlled variable-rate multimedia wideband speech codec (VMR-WB), service option 62 and 63 for spread spectrum systems," 3GPP2-C.S0052-A, vol. 1.0, Apr. 2005