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基于概率密度并联距离的话音检测算法

吴其前 张雄伟 邹霞

吴其前, 张雄伟, 邹霞. 基于概率密度并联距离的话音检测算法[J]. 电子与信息学报, 2008, 30(12): 2886-2889. doi: 10.3724/SP.J.1146.2007.00804
引用本文: 吴其前, 张雄伟, 邹霞. 基于概率密度并联距离的话音检测算法[J]. 电子与信息学报, 2008, 30(12): 2886-2889. doi: 10.3724/SP.J.1146.2007.00804
Wu Qi-Qian, Zhang Xiong-Wei, Zou Xia. Voice Activity Detection Using Parallel Distance of Probability Density[J]. Journal of Electronics and Information Technology, 2008, 30(12): 2886-2889. doi: 10.3724/SP.J.1146.2007.00804
Citation: Wu Qi-Qian, Zhang Xiong-Wei, Zou Xia. Voice Activity Detection Using Parallel Distance of Probability Density[J]. Journal of Electronics and Information Technology, 2008, 30(12): 2886-2889. doi: 10.3724/SP.J.1146.2007.00804

基于概率密度并联距离的话音检测算法

doi: 10.3724/SP.J.1146.2007.00804
基金项目: 

江苏省科学基金(BK2006001)和江苏省333高层次人才培养工程专项资助课题

Voice Activity Detection Using Parallel Distance of Probability Density

  • 摘要: 该文提出了一种基于概率密度并联距离的话音激活检测算法。算法根据语音信号和噪声信号的Mel域子带能量概率密度的不同特性,引入并联距离定义构造判决函数,通过判断该函数的值来进行语音激活检测。实验结果表明,在不同信噪比情况下,该文算法性能优于G.729B VAD算法。
  • [1] 杨行峻, 迟惠生等. 语音信号数字处理. 北京: 电子工业出版社, 1995, 第11 章. [2] Rabiner L and Juang B H. Fundamentals of SpeechRecognition. New Jersey: Prentice-Hall PTR, 1993, Chapter3. [3] Shen J L, Hung J W, and Lee L S. Robust entropy-basedendpoint detection for speech recognition in noisyenvironments [A]. ICSP 1998[C]. Sydney, Australia: 1998:232-235. [4] Benyassine A, Shlomot E, Su H Y, Massaloux D, Lamblin C,and Petit J P. ITU-T G.729 Annex B: A silence compressionscheme for use with G.729 optimized for V.70 digitalsimultaneous voice and data application. IEEE Commun Mag,1997, 35(9): 64-73. [5] Sohn J, Kim N S, and Sung W. A statistical model-basedvoice activity detection. IEEE Signal Processing Letters, 1999,6(1): 1-3. [6] Tanyer S G and Ozer H. Voice activity detection innonstationary noise[J].IEEE Trans. on Speech and AudioProcessing.2000, 8(4):478-482 [7] Gazor S and Zhang W. A soft voice activity detector based ona laplacian-gaussian model[J].IEEE Trans. on Speech andAudio Processing.2003, 11(5):498-505 [8] Ramirez J, Segura J C, and Benitez C, et al.. A newKullback-Leibler VAD for speech recognition in noise[J].IEEESignal Processing Letters.2004, 11(2):266-269 [9] Johnson D H and Sinanovics. Symmetrizing the Kullback-Leibler Distance. Technical report, Rice University, 2001. [10] Kamm T, Hermansky H, and Andreou A G. Learning theMel-scale and optimal VTN mapping, Technical Report,CSLP, Johns Hopkins University, 1997. [11] Ephraim Y and Malah D. Speech enhancement using aminimum mean-square error short-time spectral amplitudeestimator[J].IEEE Trans. on Acoustics, Speech, and SignalProcessing.1984, 32(6):1109-1121 [12] Ephraim Y and Malah D. Speech enhancement using aminimum mean-square error log-spectral amplitudeestimator[J].IEEE Trans. on Acoustics, Speech, and SignalProcessing.1985, 33(2):443-445
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  • 被引次数: 0
出版历程
  • 收稿日期:  2007-05-28
  • 修回日期:  2007-09-24
  • 刊出日期:  2008-12-19

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