Bayesian learning for time-varying linear prediction of speech

QC 20160516
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.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering hsv//eng
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

Ingår i:2015 23rd European Signal Processing Conference, EUSIPCO 2015: Institute of Electrical and Electronics Engineers (IEEE), s. 325-3299780992862633

Av författaren/redakt...: Casamitjana, Adr ...; Sundin, Martin; Ghosh, P.; Chatterjee, S.

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