Integrating RASTA-PLP into speech recognition

In previous work, we and others have shown that bandpass filtering of temporal trajectories of simple functions of the critical band spectrum can lead to more robust speech recognizers in the presence of additive and convolutional error. In this study we report results on several mechanisms for incorporating this analysis technique into training, in a way that is consistent with on-line approaches to speech recognition. In particular, we show improved robustness to these forms of degradation for a system that maps the filtered spectral points using a linear regression computed from results of the different transformations