Abstract
Nasal resonance is utilized in certain languages to differentiate word meanings. The joint filtering effect by the vocal tract and the nasal tract can be modeled by the auto-regression moving-average (ARMA) approach. However, unlike all-pole (i.e., AR) modeling, it has been difficult to derive the equivalent vocal-tract area function directly from an ARMA model due to the nonlinear nature in the relation between model coefficients and vocal-tract geometry. In this paper, we propose a method to decompose an ARMA model approximately into ?/C(z) + ?/D(z); in our context, 1/C(z) and 1/D(z) represent the filtering effects of the oral and the nasal tract, respectively. Once the decomposition is performed, equivalent oral-tract and nasal-tract area functions can be obtained by converting C(z) and D(z) to their respective lattice representation. The proposed method was applied to non-nasalized and nasalized vowels produced by three speakers, and it was found that the ratio r = ?/? tends to be higher in nasalized vowels than in their non-nasalized counterparts. The vocal-tract area function estimated by the present approach was also fairly stable for sustained vowels.