MISSING FEATURE THEORY IN ASR: MAKE SURE YOU MISS THE RIGHT TYPE OF FEATURES
J.M. de Veth, Febe de Wet, Bert Cranen, Lou Boves · 1999
In this paper we investigate acoustic backing-off as an operationalization of Missing Feature Theory to increase recognition robustness in adverse acoustic conditions. Acoustic backing-off effectively removes the detrimental influence of outlier values from the local decisions in the Viterbi algorithm. It does so without prior knowledge about the specific feature vector elements which are unreliable; thus, the technique avoids the need for explicit outlier detection. From the theory underlying Missing Feature Theory it appears that acoustic feature representations which smear local spectro-temporal distortions over all feature vector elements are inherently unsuitable. Our experiments in the context of connected digit recognition over the telephone are presented that confirm this prediction. Our results show that feature representations which minimize distortion smearing are most suited to be used in combination with Missing Feature Theory. Using additive band limited noise as a distor...