Time-Varying Modeling of Glottal Source and Vocal Tract and Sequential Bayesian Estimation of Model Parameters for Speech Synthesis

Speech is generated by articulators acting on

a phonatory source. Identification of this

phonatory source and articulatory geometry are

individually challenging and ill-posed

problems, called speech separation and

articulatory inversion, respectively.

There exists a trade-off

between decomposition and recovered

articulatory geometry due to multiple

possible mappings between an

articulatory configuration

and the speech produced. However, if measurements

are obtained only from a microphone sensor,

they lack any invasive insight and add

additional challenge to an already difficult

problem.

A joint non-invasive estimation

strategy that couples articulatory and

phonatory knowledge would lead to better

articulatory speech synthesis. In this thesis,

a joint estimation strategy for speech

separation and articulatory geometry recovery

is studied. Unlike previous

periodic/aperiodic decomposition methods that

use stationary speech models within a

frame, the proposed model presents a

non-stationary speech decomposition method.

A parametric glottal source model and an

articulatory vocal tract response are

represented in a dynamic state space formulation.

The unknown parameters of the

speech generation components are estimated

using sequential Monte Carlo methods

under some specific assumptions.

The proposed approach is compared with other

glottal inverse filtering methods,

including iterative adaptive inverse filtering,

state-space inverse filtering, and

the quasi-closed phase method.