Estimating vocal tract geometry from acoustic impedance using deep neural network

A data-driven approach using artificial neural networks is proposed to address the classic inverse area function problem, i.e., to determine the vocal tract geometry (modelled as a tube of nonuniform cylindrical cross-sections) from the vocal tract acoustic impedance spectrum. The predicted cylindrical radii and the actual radii were found to have high correlation in the three- and four-cylinder model (Pearson coefficient (ρ) and Lin concordance coefficient (ρ_c) exceeded 95%); however, for the six-cylinder model, the correlation was low (ρ around 75% and ρ_c around 69%). Upon standardizing the impedance value, the correlation improved significantly for all cases (ρ and ρ_c exceeded 90%).