## 1 Introduction
RPCA is a novel data analysis method and has been widely used for speech enhancement and robust speech representation algorithms, among which some well-known examples are briefly described here. In [30], RPCA is applied to the spectrogram of speech signals, and the resulting sparse component is shown to contain less noise and thus be noise-robust. Another speech enhancement method proposed in [31] first decomposes a speech signal into sub-bands via a wavelet transform and then uses RPCA to extract the low-rank component of the matrix created by the overlapped frames of each sub-band signal, and the final output is the inverse wavelet transform of low-rank sub-band signals. The method in [32] integrates RPCA and exemplar-based sparse representation in an SNR-dependent manner to process the spectrogram of a noise-corrupted signal, i.e., to use RPCA in a low-SNR case and use exemplar-based sparse representation in a high-SNR case. In [33; 34], RPCA is also used to decompose the spectrogram of a noise-corrupted signal, while the respective sparse component is further constrained to be a nonnegative weighted sum of pre-learned basis spectra. Briefly speaking, the algorithms in [30; 32; 33; 34] apply RPCA in the spectro-temporal (spectrogram) domain of speech signals, and in [31], RPCA is operated in the time domain of speech signals within each sub-band produced by a wavelet transform. The newly proposed method differs from the aforementioned RPCA-wise algorithms mainly in that it employs RPCA in the temporal series of FBANK/MFCC speech features, which are directly used in automatic speech recognition. Notably FBANK/MFCC features are a nonlinear transform of the spectrogram of a time-domain speech signal due to the logarithmic operation. In the proposed scenario, each signal in the training and testing sets for an ASR system is converted to FBANK/MFCC features. Then, the feature time sequence, expressed in a matrix form, is decomposed by RPCA to produce a sparse matrix and a low-rank matrix. Finally, the obtained sparse matrix is treated as the new features for the subsequent training or testing. Compared with the matrix that containsthe original features, the sparse matrix is shown to highlight the relatively fast-varying component, which very probably corresponds to the speech-dominant elements and benefits speech recognition. In comparison, the associated low-rank matrix reveals more static characteristics that are likely related to the embedded noise. We evaluate the proposed RPCA-based novel feature extraction method on the Aurora-4 benchmark task [35], which consists a medium-to-large vocabulary database and a recognition task based on the Wall Street Journal (WSJ) corpus [36]. In addition, state-of-the-art deep neural network (DNN) architecture is used for acoustic modeling in the experiments. The evaluation results show that the proposed RPCA-based method can provide the original feature with significant recognition accuracy improvement, and the achieved relative word error rate reduction can be as high as 43%. We also show that this new method can be additive to the prevalent mean normalization (MN) and relative spectral (RASTA) methods to further improve the recognition performance. As a result, these evaluation results indicate that the newly proposed method is quite promising to enhance the ASR and can broaden the corresponding applications in real environments. This paper is organized as follows. In Section 2, we briefly introduce the RPCA algorithm. Section 3 includes the proposed RPCA-based feature extraction method. The experimental setup is described in Section 4, and the experimental results as well as the corresponding discussions and analyses are given in Section 5. Finally, Section 6 contains concluding remarks and suggestions for future work.