Enhancing the sub-band modulation spectra of speech features via nonnegative matrix factorization for robust speech recognition

In this paper, we propose to enhance the modulation spectrum of speech features in noise robustness via the technique of nonnegative matrix factorization (NMF). With NMF, a set of nonnegative basis spectra vectors is derived from the clean speech to represent the important components for speech recognition. However, compared with the original NMF-based scheme that employs iterative search to update the full-band modulation spectra, we propose to apply the orthogonal projection to update the low sub-band modulation spectra. In contrast to the original scheme, the presented new process significantly reduces the computation complexity without the cost of degraded recognition performance. In the experiments conducted on the Aurora-2 database, we show that the presented new NMF-based approach can provide an average error reduction rate of over 65% relative as compared with the baseline MFCC system.