## 1 Introduction  
Automatic speech recognition (ASR) applications have been widely seen in our daily life, and some examples include voice-based command controls of a robot, speech recognition using mobile devices and speech-related web search. However, an ASR system usually degrades significantly in performance when it is applied to an environment with interferences such as additive noise and channel distortion. In recent decades, researchers have been focused on developing noise-robust methods in order to compensate for the aforementioned interference effects and enhance the ASR performance, and these methods can be roughly classified into two fields: feature-based and model-based. Generally speaking, most of the feature-based methods are developed to enhance the noise-robust ability of existing and widely used speech features like perceptual linear prediction coefficients (PLP) [1], mel-frequency cepstral coefficients (MFCC) [2], logarithmic mel-filter-bank coefficients (FBANK) [2], and Gammatone frequency cepstral coefficients [3], and some of them act at the intermediate stage of the creating process of speech features. For example, spectral subtraction (SS) [4, 5], Wiener filtering [6], and MMSE-based short-time spectral amplitude estimation [7, 8] are exemplary methods that process the frame-wise acoustic spectra, which are then converted to final features for speech recognition. Second, a variety of statistical moment normalization methods are developed and conducted on the intermediate and final stages of creating speech features to give significant improvement in recognition accuracy under noise-corrupted situations, such as mean normalization (MN) [9], mean and variance normalization (MVN) [10], and histogram normalization (HEQ) [11; 12], to name but a few. Furthermore, because the aforementioned statistical moments are directly evaluated by the temporal series of speech features, these moment normalization methods implicitly enhance speech features with regard to temporal characteristics. By contrast, another direction of feature enhancement directly and explicitly processes the temporal series of speech features, and the respective methods include, but are not limited to, RASTA [13], temporal structure normalization (TSN) [14], and MVN plus ARMA filtering (MVA) [15]. Additionally, the methods of spectral histogram equalization (SHE) [16], modulation spectrum replacement/filtering (MSR/MSF) [17], and nonnegative matrix factorization (NMF)-based modulation spectrum enhancement [18] directly modify modulation spectra, which are specifically referred to as the Fourier transform of the feature time sequence. On the other hand, the model-based methods attempt to adapt the existing acoustic models with noise information to make them more suitable for an application environment. According to [19], these methods can be further split into two schools: general adaptation and noise-specific compensation. The general-adaptation methods use a generic transformation to convert acoustic model parameters, and some representative methods include maximum-likelihood linear regression (MLLR) [20], maximum likelihood linear transform (MLLT) [21], minimum classification error-based linear regression (MCELR) [22; 23] and discriminative mapping transform [24]. By contrast, the noise-specific compensation methods update acoustic model parameters by explicitly adopting the characteristics of the noise present in an application environment, and they include parallel model combination (PMC) [25] and model-based vector Taylor series (VTS) [26]. Interested readers are referred to [19; 27] for a comprehensive coverage of recent noise-robust techniques for automatic speech recognition. In this paper, we propose a feature-based method that uses the technique of robust principal component analysis (RPCA) [28; 29] aiming to extract noise-robust speech features.