Acoustic Pre-Processing For Optimal Effectivity Of Missing Feature Theory

In this paper we investigate acoustic backing-off as an operationalization of Missing Feature Theory with the aim to increase recognition robustness. Acoustic backing-off effectively diminishes the detrimental influence of outlier values by using a new model of the probability density function of the feature values. The technique avoids the need for explicit outlier detection. Situations that are handled best by Missing Feature Theory are those where only part of the coefficients are disturbed and the rest of the vector is unaffected. Consequently, one may predict that acoustic feature representations that smear local spectrotemporal distortions over all feature vector elements are inherently less suitable for automatic speech recognition. Our experiments seem to confirm this prediction. Using additive band limited noise as a distortion and comparing four different types of feature representations, we found that the best recognition performance is obtained with recognizers that use acoustic backingoff and that operate on feature types that minimally smear the distortion