Robust speech recognition using features based on zero crossings with peak amplitudes

The paper presents an extensive study of zero crossings with peak amplitudes (ZCPA) features, that have earlier been shown to outperform both conventional and auditory-based features in the presence of additive noise. The study starts by optimizing different parameters involved in ZCPA feature computation, followed by a comparison of ZCPA and MFCC features on two recognition tasks in different background conditions. The main differences between the two feature types are identified, and their individual effects on ASR performance are evaluated. The importance of a proper choice of analysis frame lengths and filter bandwidths in ZCPA feature extraction is demonstrated. Furthermore, the use of dominant frequency information in ZCPA features is found to be a major reason for increased robustness of ZCPA features compared to MFCC features.