articulatorytoacoustic

Articulatorytoacoustic refers to the field that studies and implements the relationship between the configuration of the human vocal tract during speech production and the resulting acoustic signal. In practice, it involves models that take articulatory measurements—such as tongue and lip positions, jaw height, velum opening, and laryngeal activity—and generate the corresponding waveform or spectro-temporal representation.

Forward articulatorytoacoustic modeling is used in articulatory synthesis: constructing synthetic speech by controlling articulatory parameters. Inverse problems estimate articulatory trajectories from observed acoustics or from silent or whispered speech, a task known as articulatory inversion or acoustic-to-articulatory mapping.

Data and measurements: studies rely on synchronized audio with articulatory data obtained through imaging and tracking methods, including ultrasound tongue imaging, electromagnetic articulography (EMA), magnetic resonance imaging (MRI), X-ray microbeam, and palatography. Datasets such as MOCHA-TIMIT and XRMB have supported research.

Modeling approaches: analytical vocal-tract models (for example, the Maeda model) parameterize vocal-tract geometry; data-driven approaches use regression, Gaussian processes, or neural networks to map between articulatory features and acoustics; hybrid approaches combine physical models with statistical learning.

Applications and challenges: applications include high-quality articulatory synthesis, silent speech interfaces, robust automatic speech recognition with articulatory features, and linguistics research. Challenges include limited and noisy articulatory data, cross-speaker variability, coarticulation, and the ill-posed nature of inversion.