噪聲自適應(yīng)的多數(shù)據(jù)流復(fù)合子帶語音識別方法
Noise Adaptive Multi-stream Hybrid Sub-band Approach for Robust Speech Recognition
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摘要: 首先針對現(xiàn)有丟失數(shù)據(jù)語音識別技術(shù)中的邊緣化(marginalisation)技術(shù)在特征運用上的局限，提出了一種倒譜特征分量的可靠性估計方法，將邊緣化技術(shù)推廣到常用的倒譜語音識別系統(tǒng)中; 然后利用基于全帶和子帶倒譜特征的邊緣化識別器在不同噪聲中的互補性能，提出了一種噪聲自適應(yīng)的多數(shù)據(jù)流復(fù)合子帶語音識別方法。實驗結(jié)果表明，所提識別方法可以自適應(yīng)地選出全帶和子帶數(shù)據(jù)流中受噪聲影響較小者并以之為主要依據(jù)進行識別，有效地提高了識別系統(tǒng)在多變噪聲環(huán)境中的魯棒性。Abstract: This paper first proposes a new method for evaluating the reliability of cepstral components and extends the marginalisation technique to cepstral recognizers. Then a noise adaptive multi-stream hybrid sub-band approach is proposed for robust speech recognition by making use of the complemental performances between full-band and sub-band cepstral marginalisation recognizers in different noises. Experimental results show that the proposed approach can turn to the less distorted data stream automatically and improve the robustness of the speech recognizer in various noisy environments effectively.
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Mendel J M. White-noise estimators for seismic data processing in oil exploration[J]. IEEE Trans. Automatic Control, 1977, 22(5): 694706. .[2]Mendel J M. Optimal Seismic Deconvolution：an Estimation- based Approach[M]. New York：Academic Press，1983: 1.103.[3]Sun Shuli. Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor[J].Automatica.2004, 40 (8):1447-[4]Sun Shuli，Deng Zili. Multi-sensor optimal information fusion Kalman filter[J]. Automatica, 2004, 40(6): 10171023. .[5]鄧自立，高媛，李云等. 基于Kalman濾波的信息融合白噪聲最優(yōu)反卷積濾波器[J]. 科學(xué)技術(shù)與工程，2004，4(3)：169.171.[6]鄧自立. 卡爾曼濾波與維納濾波現(xiàn)代時間序列分析方法[M]. 哈爾濱：哈爾濱工業(yè)大學(xué)出版社，2000: 1.396.[7]鄧自立. 自校正濾波理論及其應(yīng)用現(xiàn)代時間序列分析方法[M]. 哈爾濱：哈爾濱工業(yè)大學(xué)出版社，2003: 1.343. -
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