Forward masking for increased robustness in automatic speech recognition

In automatic speech recognition mel-frequency cepstral coefficients (MFCC) or linear predictive cepstral coefficients (LPCC) are features commonly used today. However, their calculation considers only a few features of the auditory system. On the assumption that the human representation of speech is an optimal representation, considering more features of the auditory system might lead to a better performance of automatic speech recognition systems. In this paper a model proposed by Strope and Alwan [1], which relies on the human acoustic perception and allows to consider the effect of forward masking, is incorporated after some modifications into an automatic speech recognition system with a MFCC-based front-end. The extended system is evaluated on recognition tasks, that are closer to real recognition than (connected) digit recognition commonly used in the literature. The evaluations show an increased robustness of the speech recognition system with forward masking on all recognition tasks, but especially on data recorded in noisy environments.