Although the field of automatic speaker or speech recognition has been extensively studied over the past decades,\nthe lack of robustness has remained a major challenge. The missing data technique (MDT) is a promising approach.\nHowever, its performance depends on the correlation across frequency bands. This paper presents a new\nreconstruction method for feature enhancement based on the trait. In this paper, the degree of concentration across\nfrequency bands is measured with principal component analysis (PCA). Through theoretical analysis and experimental\nresults, it is found that the correlation of the feature vector extracted from the sub-band (SB) is much stronger than\nthe ones extracted from the full-band (FB). Thus, rather than dealing with the spectral features as a whole, this paper\nsplits full-band into sub-bands and then individually reconstructs spectral features extracted from each SB based on\nMDT. At the end, those constructed features from all sub-bands will be recombined to yield the conventional\nmel-frequency cepstral coefficient (MFCC) for recognition experiments. The 2-sub-band reconstruction approach is\nevaluated in speaker recognition system. The results show that the proposed approach outperforms full-band\nreconstruction in terms of recognition performance in all noise conditions. Finally, we particularly discuss the optimal\nselection of frequency division ways for the recognition task. When FB is divided into much more sub-bands, some of\nthe correlations across frequency channels are lost. Consequently, efficient division ways need to be investigated to\nperform further recognition performance.
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