A strategy for correcting errors in automated formant extraction

Abstract

Sociophonetic vowel analysis relies heavily on measurements of resonant frequencies, particularly of the first and second formants. Automated formant estimation using linear predictive coding (LPC) algorithms in software like Praat greatly increases efficiency compared to hand measurements and allows researchers to analyze more data than was possible before this technological advancement. However, LPC analysis is prone to certain types of errors (e.g., Di Paolo et al., 2011; Harrison, 2013; Labov et al., 2006; Strelluf, 2019; Styler, 2017). In one common error, which I call “faulty low F2” (FLF2), LPC identifies a spectral peak between F1 and F2 as an F2 measurement. In automated extraction, the real F2 is then recorded as F3. Manually correcting these errors is time-consuming, potentially erasing the gains made by automated extraction. However, a systematic error suggests the possibility of a systematic solution. This presentation describes a method for identifying and correcting FLF2 errors using a script in R. The script identifies possible errors based on expected ranges and trajectories, determines whether the recorded F3 at the measurement point meets the criteria for a legitimate F2, and makes substitutions where appropriate. Results of tests with real data are presented. [Work supported by NSF #XXXXXXX.]