The Use of Dictionary Learning Approach for Robustness Speech Recognition

The performance of automatic speech recognition (ASR) often degrades dramatically in noisy environments. In this paper, we present a novel use of dictionary learning approach to normalizing the magnitude modulation spectra of speech features so as to retain more noise-resistant and important acoustic characteristics. To this end, we employ the K-SVD method to create sparse representations for a common set of basis vectors that span the intrinsic temporal structure inherent in the modulation spectra of clean training speech features. In addition, taking into account the non-negativity property of amplitude modulation spectrum, we utilize the nonnegative K-SVD method, paired with the nonnegative sparse coding method, to capture more noise-robust features. All experiments were conducted on the Aurora-2 corpus and task. The empirical evidence shows that our methods can offer substantial improvements over the baseline NMF method. Finally, we also integrate the proposed variants of the K-SVD method with other well-known robustness methods like Advanced Front-End (AFE), Cepstral Mean and Variance Normalization (CMVN) and Histogram Equalization (HEQ) to further confirm their utility.