An algorithm for token-by-token linear predictive coding analysis of oral monophthongs.

Abstract

Linear Predictive Coding (LPC) is the most reliable vowel formant estimation method. LPC trajectories closely approximate formants and are preferable as a means of avoiding human bias. Nevertheless, formant analysis by LPC is not error free, as it can be affected by fundamental frequency, noise and vocal tract morphology. Error minimization is usually achieved through manual checking of LPC results with spectral contour. An algorithm to find optimal LPC parameters (Burg method) on a token by token basis was developed and implemented as a Praat script. The algorithm minimizes inter- and intra-vowel formant variability without knowledge of vowel identity. Algorithm results were compared with results of manual adjustment of LPC parameters on a trial and error basis on a corpus of 180 stressed and unstressed oral monophthongs of Brazilian Portuguese from a male speaker. First and second formants linear correlations are r2 = 0.98 and 0.99 respectively. The areas of /i, a, u/ vocalic triangles (mean values as vertices) of manual and automatic measurements showed only 4.5% difference. These results suggest the algorithm can be generalized to any speaker and speed up the measurement of vowel formants in large corpora. [Work supported by CNPq and Fapesp.]