ISCA Archive Interspeech 2008
ISCA Archive Interspeech 2008

Sparse linear predictors for speech processing

Daniele Giacobello, Mads Græsbøll Christensen, Joachim Dahl, Søren Holdt Jensen, Marc Moonen

This paper presents two new classes of linear prediction schemes. The first one is based on the concept of creating a sparse residual rather than a minimum variance one, which will allow a more efficient quantization; we will show that this works well in presence of voiced speech, where the excitation can be represented by an impulse train, and creates a sparser residual in the case of unvoiced speech. The second class aims at finding sparse prediction coefficients; interesting results can be seen applying it to the joint estimation of long-term and short-term predictors. The proposed estimators are all solutions to convex optimization problems, which can be solved efficiently and reliably using, e.g., interior-point methods.

doi: 10.21437/Interspeech.2008-394

Cite as: Giacobello, D., Christensen, M.G., Dahl, J., Jensen, S.H., Moonen, M. (2008) Sparse linear predictors for speech processing. Proc. Interspeech 2008, 1353-1356, doi: 10.21437/Interspeech.2008-394

  author={Daniele Giacobello and Mads Græsbøll Christensen and Joachim Dahl and Søren Holdt Jensen and Marc Moonen},
  title={{Sparse linear predictors for speech processing}},
  booktitle={Proc. Interspeech 2008},