Sparse linear prediction and its applications to speech processing

Daniele Giacobello, Mads Grsbll Christensen, Manohar N. Murthi, Sren Holdt Jensen, Marc Moonen

Research output: Contribution to journalReview articlepeer-review

105 Scopus citations


The aim of this paper is to provide an overview of Sparse Linear Prediction, a set of speech processing tools created by introducing sparsity constraints into the linear prediction framework. These tools have shown to be effective in several issues related to modeling and coding of speech signals. For speech analysis, we provide predictors that are accurate in modeling the speech production process and overcome problems related to traditional linear prediction. In particular, the predictors obtained offer a more effective decoupling of the vocal tract transfer function and its underlying excitation, making it a very efficient method for the analysis of voiced speech. For speech coding, we provide predictors that shape the residual according to the characteristics of the sparse encoding techniques resulting in more straightforward coding strategies. Furthermore, encouraged by the promising application of compressed sensing in signal compression, we investigate its formulation and application to sparse linear predictive coding. The proposed estimators are all solutions to convex optimization problems, which can be solved efficiently and reliably using, e.g., interior-point methods. Extensive experimental results are provided to support the effectiveness of the proposed methods, showing the improvements over traditional linear prediction in both speech analysis and coding.

Original languageEnglish (US)
Article number6145743
Pages (from-to)1644-1657
Number of pages14
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number5
StatePublished - 2012


  • 1-norm minimization
  • compressed sensing
  • linear prediction
  • sparse representation
  • speech analysis
  • speech coding

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics


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