基於離散餘弦轉換之語音特徵的強健性補償法

Compensating the speech features via discrete cosine transform for robust speech recognition

In this paper, we develop a series of algorithms to improve the noise robustness of speech features based on discrete cosine transform (DCT). The DCT-based modulation spectra of clean speech feature streams in the training set are employed to generate two sequences representing the reference magnitudes and magnitude weights, respectively. The two sequences are then used to update the magnitude spectrum of each feature stream in the training and testing sets. The resulting new feature streams have shown robustness against the noise distortion. The experiments conducted on the Aurora-2 digit string database reveal that the proposed DCT-based approaches can provide relative error reduction rates of over 25% as compared with the baseline system using MVN-processed MFCC features. Experimental results also show that these new algorithms are well additive to many noise robustness methods to produce even higher recognition accuracy rates.