Normalization of nonlinearly time-dynamic vowels

Abstract

This study compares 16 vowel-normalization methods for purposes of sociophonetic research. Most of the previous work in this domain has focused on the performance of normalization methods on steady-state vowels. By contrast, this study explicitly considers dynamic formant trajectories, using generalized additive models to model these nonlinearly. Normalization methods were compared using a hand-corrected dataset from the Flemish-Dutch Teacher Corpus, which contains 160 speakers from 8 geographical regions, who spoke regionally accented versions of Netherlandic/Flemish Standard Dutch. Normalization performance was assessed by comparing the methods' abilities to remove anatomical variation, retain vowel distinctions, and explain variation in the normalized F0–F3. In addition, it was established whether normalization competes with by-speaker random effects or supplements it, by comparing how much between-speaker variance remained to be apportioned to random effects after normalization. The results partly reproduce the good performance of Lobanov, Gerstman, and Nearey 1 found earlier and generally favor log-mean and centroid methods. However, newer methods achieve higher effect sizes (i.e., explain more variance) at only marginally worse performances. Random effects were found to be equally useful before and after normalization, showing that they complement it. The findings are interpreted in light of the way that the different methods handle formant dynamics.