A regression approach to vowel normalization for missing and unbalanced data

Researchers investigating the vowel systems of languages or dialects frequently employ normalization methods to minimize between-speaker variability in formant patterns while preserving between-phoneme separation and (socio-)dialectal variation. Here two methods are considered: log-mean and Lobanov normalization. Although both of these methods express formants in a speaker-dependent space, the methods differ in their complexity and in their implied models of human vowel-perception. Typical implementations of these methods rely on balanced data across speakers so that researchers may have to reduce the data available in the analyses in missing-data situations. Here, an alternative method is proposed for the normalization of vowels using the log-mean method in a linear-regression framework. The performance of the traditional approaches to log-mean and Lobanov normalization against the regression approach to the log-mean method using naturalistic, simulated vowel-data was investigated. The results indicate that the Lobanov method likely removes legitimate linguistic variation from vowel data and often provides very noisy estimates of the actual vowel quality associated with individual tokens. The authors further argue that the Lobanov method is too complex to represent a plausible model of human vowel perception, and so is unlikely to provide results that reflect the true perceptual organization of linguistic data.