An Approach Using Combination of Multiple Features through Sigmoid Function for Speech-Presence/Absence Discrimination

この論文にアクセスする

この論文をさがす

著者

抄録

In this paper, we present an approach of detecting speech presence for which the decision rule is based on a combination of multiple features using a sigmoid function. A minimum classification error (MCE) training is used to update the weights adjustment for the combination. The features, consisting of three parameters: the ratio of ZCR, the spectral energy, and spectral entropy, are combined linearly with weights derived from the sub-band domain. First, the Bark-scale wavelet decomposition (BSWD) is used to split the input speech into 24 critical sub-bands. Next, the feature parameters are derived from the selected frequency sub-band to form robust voice feature parameters. In order to discard the seriously corrupted frequency sub-band, a strategy of adaptive frequency sub-band extraction (AFSE) dependant on the sub-band SNR is then applied to only the frequency sub-band used. Finally, these three feature parameters, which only consider the useful sub-band, are combined through a sigmoid type function incorporating optimal weights based on MSE training to detect either a speech present frame or a speech absent frame. Experimental results show that the performance of the proposed algorithm is superior to the standard methods such as G.729B and AMR2.

収録刊行物

  • IEICE transactions on fundamentals of electronics, communications and computer sciences

    IEICE transactions on fundamentals of electronics, communications and computer sciences 94(8), 1630-1637, 2011-08-01

    一般社団法人 電子情報通信学会

参考文献:  24件中 1-24件 を表示

各種コード

  • NII論文ID(NAID)
    10030190343
  • NII書誌ID(NCID)
    AA10826239
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168508
  • データ提供元
    CJP書誌  J-STAGE 
ページトップへ