Bayesian learning for time-varying linear prediction of speech

• We develop Bayesian learning algorithms for estimation of time-varying linear prediction (TVLP) coefficients of speech. Estimation of TVLP coefficients is a naturally underdeter-mined problem. We consider sparsity and subspace based approaches for dealing with the corresponding underde-termined system. Bayesian learning algorithms are developed to achieve better estimation performance. Expectation-maximization (EM) framework is employed to develop the Bayesian learning algorithms where we use a combined prior to model a driving noise (glottal signal) that has both sparse and dense statistical properties. The efficiency of the Bayesian learning algorithms is shown for synthetic signals using spectral distortion measure and formant tracking of real speech signals.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

Bayesian learning

expectation-maximization

sparsity

Time-varying linear prediction

Algorithms

Forecasting

Maximum principle

Signal processing

Estimation performance

Expectation - maximizations

Expectation Maximization

Linear prediction

Spectral distortions

Statistical properties

Learning algorithms

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)