ISCA Archive Eurospeech 1999
ISCA Archive Eurospeech 1999

Acoustic pre-processing for optimal effectivity of missing feature theory

Johan de Veth, Bert Cranen, Febe de Wet, Louis Boves

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 spectro-temporal 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 backing-off and that operate on feature types that minimally smear the distortion.


doi: 10.21437/Eurospeech.1999-20

Cite as: Veth, J.d., Cranen, B., Wet, F.d., Boves, L. (1999) Acoustic pre-processing for optimal effectivity of missing feature theory. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 65-68, doi: 10.21437/Eurospeech.1999-20

@inproceedings{veth99_eurospeech,
  author={Johan de Veth and Bert Cranen and Febe de Wet and Louis Boves},
  title={{Acoustic pre-processing for optimal effectivity of missing feature theory}},
  year=1999,
  booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
  pages={65--68},
  doi={10.21437/Eurospeech.1999-20}
}