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 realtime 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.