A STUDY ON CEPSTRAL SUB-BAND NORMALIZATION FOR ROBUST ASR

In this paper, we propose a cepstral subband normalization (CSN) approach for robust speech recognition. The CSN approach first applies the discrete wavelet transform (DWT) to decompose the original cepstral feature sequence into low and high frequency band (LFB and HFB) parts. Then, CSN normalizes the LFB components and zeros out the HFB components. Finally, an inverse DWT is applied on LFB and HFB components to form the normalized cepstral features. When using the Haar functions as the DWT bases, the calculation of CSN can be processed efficiently with a 50% reduction on the amount of feature components. In addition, our experimental results on the Aurora-2 task show that CSN outperforms the conventional cepstral mean subtraction (CMS), cepstral mean and variance normalization (CMVN), and histogram equalization (HEQ). We also integrate CSN with advanced front-end (AFE) for feature extraction. Experimental results indicate that the integrated AFE+CSN achieves notable improvements over the original AFE. The simple calculation, compact in form, and effective noise robustness properties enable CSN to perform suitably for mobile applications.