Multi-Layer Channel Normalization for Frequency-Dynamic Feature Extraction
频域动态特征提取中的多层信道正规化

Despite the steady progress made in the area of speech recognition and a high number of practicalapplications, it is widely acknowledged that recognition technology today is not at the desired level. One mainobstacle is what said "robustness". This paper focus on one popular idea in antagonizing speech systemvulnerability-channel normalization, and presents a new normalization algorithm MLCN (multi-layer channelnormalization), which exploits the recursive compensation progress in two domains (spectral domain and cepstraldomain) to depress the noise and channel distortion, so that the more robust speech representation for the followingprocessing is achieved. A new frequency-dynamic feature extraction algorithm is also proposed due to theintroduction of MLCN, which allows dynamic information integrated in the final feature vectors. Experimentalresults of the gallina system demonstrate the validity of the new algorithm.