Noise suppression based on nonnegative matrix factorization for robust speech recognition

This paper presents a novel noise robustness method, nonnegative matrix factorization-based noise suppression (NNS), to enhance the magnitude spectrum of speech signals for better speech recognition performance in noise-corrupted environments. In the presented approach, the clean data and noise in the training set are firstly converted to the spectrograms via short-time Fourier transform (STFT), and the basis spectral matrices of the speech data and noise are learned from the corresponding spectrograms accordingly. Then, the magnitude spectrogram of the noise-corrupted testing data is factorized via the basis matrices of the clean data, and the resulting noise components are alleviated from the original magnitude spectrogram. Finally, the new noise-reduced magnitude spectrogram is integrated with the original noisy phase spectrogram and then converted back to a timedomain signal, which is subsequently converted to a sequence of MFCC speech features. By using the presented NNS as a pre-processing stage of the speech recognition system, the obtained recognition accuracy can outperform the MFCC baseline especially at median and low SNR cases. Furthermore, performing NNS on the different sub-band spectrograms can further improve the recognition results relative to the original NNS performing on the full-band spectrogram, indicating that sub-band NNS can produce more robust speech features suitable for noisy speech recognition.