To date, Malaysian English (MalE) accents arises from different ethnics of its populace are scarcely investigated using empirical methods that give a decisive conclusion to treat MalE as either uniform or non-uniform variety. The popularly used Mel-frequency cepstral coefficients (MFCC) as feature extractor fails to perform well under noisy conditions. This paper proposes two new methods and noise less-susceptible feature extractors to mitigate the deficiency of MFCC. Statistical descriptors of Mel-bands spectral energy (MBSE) is an enhancement of traditional filter-bank analysis, however, increases fourfold as much the feature size. This issue is tackled by proposing a transformation using principle component analysis to generate a new PCA-MBSE feature set. Experimental results indicated promising accuracy rates of 94.6% and 89.9% to recognize between the Malay, Chinese and Indian accents of MalE speech for male and female datasets respectively. Under severe noisy conditions, however, MFCC features started to deteriorate faster than MBSE-based features. PCA-MBSE features showed the most robust quality where its performance was just slightly deteriorated by 17.1% (male dataset) and 13.6% (female dataset) as compared to MBSE of 33.1%and 31.3% while worst results of deterioration were obtained for MFCC of 35.7% and 36.8% for the male and female datasets respectively.