Approach of feature with confident weight for robust speech recognition

Enhancement of robustness has become one of research focuses of acoustic speech recognition system. In recent works, Missing Feature Theory (MFT) has been proved an available and considerable solution for robust speech recognition based on either ignoring or compensating the unreliable components of feature vectors corrupted mainly by band-limited background noise. Because of MIFA classifying in binary way and necessarily of dealing with the cepstral feature, this paper proposes three new approaches based on confidence analysis. Approach of Feature with Confident Weight(AFCW) estimates the confidence of each feature component as its weight and describes the effect of noise in a more precise way The other two approaches, SC(Simple Cepstral)- and TC(Total Cepstral)-AFCW, can be regarded as AFCW on cepstral domain. Experimental results show proposed approaches could improve the recognition accuracy significantly in adverse environment, including stationary and nonstationary noise environments.