Real-time robust formant estimation system using a phase equalization-based autoregressive exogenous model

Abstract

This paper presents a real-time robust formant tracking system for speech using a real-time phase equalization-based autoregressive exogenous model (PEAR) with electroglottography (EGG). Although linear predictive coding (LPC) analysis is a popular method for estimating formant frequencies, it is known that the estimation accuracy for speech with high fundamental frequency F₀ would be degraded since the harmonic structure of the glottal source spectrum deviates more from the Gaussian noise assumption in LPC as its F₀ increases. In contrast, PEAR, which employs phase equalization and LPC with an impulse train as the glottal source signals, estimates formant frequencies robustly even for speech with high F₀. However, PEAR requires higher computational complexity than LPC. In this study, to reduce this computational complexity, a novel formulation of PEAR was derived, which enabled us to implement PEAR for a real-time robust formant tracking system. In addition, since PEAR requires timings of glottal closures, a stable detection method using EGG was devised. We developed the real-time system on a digital signal processor and showed that, for both the synthesized and natural vowels, the proposed method can estimate formant frequencies more robustly than LPC against a wider range of F₀.