Speech recognition features: Comparison studies on robustness against environmental distortions

The robustness against environmental distortions of various features used in speech recognition: MFCC, PLP, LPCC, FBANK, MELSPEC, ETSI - AFE, and PNCC are compared in this paper. These features are evaluated on Aurora-2, English spoken digit recognition task, a popular corpus often used to evaluate the robustness of speech recognition approaches. The results show that the use of different types of filter bank such as mel-scale filter bank in MFCC and Bark scale filter bank in PLP, achieves similar performance. The robustness of speech recognition features against environmental distortions are improved by using DCT even though the performances of features with and without DCT are comparable in clean conditions. PNCC, the current state-of-the-art feature generally shows a better performance compared to traditional features, except ETSI - AFE. Need to be noted that ETSI - AFE is found to be bias on Aurora-2 task.