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Speaker-Specific Adaptation Of Maeda Synthesis Parameters For Auditory Feedback, Joseph Vonderhaar

Master's Theses (2009 -)

The Real-time Articulatory Speech Synthesizer (RASS) is a research tool in the Marquette Speech and Swallowing lab that simultaneously collects acoustic and articulatory data from human participants. The system is used to study acoustic-to-articulatory inversion, articulatory-to-acoustic synthesis mapping, and the effects of real-time acoustic feedback. Electromagnetic Articulography (EMA) is utilized to collect position data via sensors placed in a subject’s mouth. These kinematic data are then converted into a set of synthesis parameters that controls an articulatory speech synthesizer, which in turn generates an acoustic waveform matching the associated kinematics. Independently from RASS, the synthesized acoustic waveform can be further …