Noise Subspace Fuzzy C-means Clustering for Robust Speech Re(8)

时间:2025-07-11

Abstract. In this paper a fuzzy C-means (FCM) based approach for speech/non-speech discrimination is developed to build an effective voice activity detection (VAD) algorithm. The proposed VAD method is based on a soft-decision clustering approach built ove

Table 1. Average word accuracy (%) for the Spanish SDC database. Base 92.94 83.31 51.55 75.93 Woo WM 95.35 MM 89.30 HM 83.64 Average 89.43 WM MM HM Average G.729 AMR1 AMR2 AFE 88.62 94.65 95.67 95.28 72.84 80.59 90.91 90.23 65.50 62.41 85.77 77.53 75.65 74.33 90.78 87.68 Li Marzinzik Sohn FCM-VAD 91.82 94.29 96.07 96.68 77.45 89.81 91.64 91.82 78.52 79.43 84.03 86.05 82.60 87.84 90.58 91.512. ITU, A silence compression scheme for G.729 optimized for terminals conforming to recommendation V.70, 1996, ITU-T Recommendation G.729-Annex B. 3. J. Sohn, N. S. Kim and W. Sung, A statistical model-based voice activity detection, 1999, IEEE Signal Processing Letters,vol 16,num 1, pages 1-3,. 4. R. L. Bouquin-Jeannes and G. Faucon, Study of a voice activity detector and its in uence on a noise reduction system, 1995, Speech Communication, vol 16, pages 245-254. 5. K. Woo, T. Yang, K. Park and C. Lee, Robust voice activity detection algorithm for estimating noise spectrum, 2000, Electronics Letters, vol 36, num 2, pages 180-181. 6. Q. Li, J. Zheng, A. Tsai and Q. Zhou, Robust endpoint detection and energy normalization for real-time speech and speaker recognition, 2002, IEEE Transactions on Speech and Audio Processing, vol 10, num 3, pages 146-157. 7. M. Marzinzik and B. Kollmeier, Speech pause detection for noise spectrum estimation by tracking power envelope dynamics, 2002, IEEE Transactions on Speech and Audio Processing, vol 10, num 6, pages 341-351. 8. J. Ram´ rez, Jos´ C. Segura, C. Ben´ e tez, L. Garc´ and A. Rubio, Statistical Voice a Activity Detection using a Multiple Observation Likelihood Ratio Test, 2005, IEEE Signal Processing Letters, vol 12, num 10, pages 689-692. 9. Anderberg, M. R. 1973. Cluster Analysis for Applications. Academic Press, Inc., New York, NY. 10. Rasmussen, E. 1992. Clustering algorithms. In Information Retrieval: Data Structures and Algorithms, W. B. Frakes and R. Baeza-Yates, Eds. Prentice-Hall, Inc., Upper Saddle River, NJ, 419-442 11. Jain, A. K. and Dubes, R. C. 1988. Algorithms for Clustering Data. Prentice-Hall advanced reference series. Prentice-Hall, Inc., Upper Saddle River, NJ. 12. J. Ram´ rez, Jos´ C. Segura, C. Ben´ e tez, A. de la Torre, A. Rubio, An E ective Subband OSF-based VAD with Noise Reduction for Robust Speech Recognition, 2005, In press IEEE Trans. on Speech and Audio Processing. 13. J. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters, J. Cybern., vol. 3, no. 3, pp. 32-57, 1974. 14. J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981. 15. A. Moreno, L. Borge, D. Christoph, R. Gael, C. Khalid, E. Stephan and A. Je rey, SpeechDat-Car: A Large Speech Database for Automotive Environments, Proceedings of the II LREC Conference, 2000.

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