Estimation of Glottal Closing and Opening Instants in Voiced Speech Using the YAGA Algorithm

Abstract

Abstract-Accurate estimation of glottal closing instants (GCIs) and opening instants (GOIs) is important for speech processing applications that benefit from glottal-synchronous processing including pitch tracking, prosodic speech modification, speech dereverberation, synthesis and study of pathological voice. We propose the Yet Another GCI/GOI Algorithm (YAGA) to detect GCIs from speech signals by employing multiscale analysis, the group delay function, and -best dynamic programming. A novel GOI detector based upon the consistency of the candidates' closed quotients relative to the estimated GCIs is also presented. Particular attention is paid to the precise definition of the glottal closed phase, which we define as the analysis interval that produces minimum deviation from an all-pole model of the speech signal with closedphase linear prediction (LP). A reference algorithm analyzing both electroglottograph (EGG) and speech signals is described for evaluation of the proposed speech-based algorithm. In addition to the development of a GCI/GOI detector, an important outcome of this work is in demonstrating that GOIs derived from the EGG signal are not necessarily well-suited to closed-phase LP analysis. Evaluation of YAGA against the APLAWD and SAM databases show that GCI identification rates of up to 99.3% can be achieved with an accuracy of 0.3 ms and GOI detection can be achieved equally reliably with an accuracy of 0.5 ms.Index Terms-Dynamic programming, electroglottograph (EGG), glottal closing instants (GCIs), glottal opening instants (GOIs), group delay function, multiscale analysis, speech processing.

An efficient solution to sparse linear prediction analysis of speech

“…This can also be explained with regard to physical production mechanism of the speech signal: as the coupling of excitation source and vocal tract filter is maximized on GCIs, such weighting function assists the minimizer to exclude the points on which the coupling is maximized and concentrate its effort on speech samples where the source contribution is minimized. Such decoupling is investigated in the context of Glottal volume velocity estimation by closed phase inverse filtering techniques [16]. There, the whole time interval on which the glottis is expected to be open is localized and discarded from the analysis frame.…”

“…Consequently, these methods require the availability of both GCI and Glottal opening instants (GOI). However, the determination of GOIs is much more difficult than GCI detection [16]. Moreover, as the analysis window is strictly limited to the closed phase [17], another practical issue may arise: this time-frame might be too short (for high-pitched voices for instance) such that the analysis becomes ineffective [16].…”

A novel filterbank for epoch estimation

“…Linear prediction (LP) analysis combined with some preprocessing such as epoch filtering of linear prediction residual (LPR) [5], unwrapped phase spectrum of short-time Fourier Transform (STFT) of LPR [6], Hilbert envelope of LPR and group delay function [7], has been found useful for epoch estimation. Methods like YAGA [8], DYPSA [9] have employed dynamic programming to reduce the insertions suffered by group delaybased methods. The approaches like ZFR [4], SEDREAMS [10] and recently proposed novel filtering-based approach (FBA) [11] use smoothing of the speech signal to detect epoch locations, getting rid from parameter settings required in LP analysis.…”

A Fast Method for High-Resolution Voiced/Unvoiced Detection and Glottal Closure/Opening Instant Estimation of Speech

“…Several methods have been proposed for GCI estimation [2], [21]- [28], but only a few for both GCI and GOI [8], [9], [29]- [32] or GOI only estimation [33]. To the authors' knowledge, the sliding linear prediction covariance analysis [8] was the first method proposed for GCI/GOI estimation.…”

“…The latter is a smoothed, windowed version of the speech signal and the window length is a function of the mean pitch of the speech signal. The yet another GCI/GOI algorithm (YAGA) [32] follows a similar strategy to DYPSA based on the phase slope function and on N -best dynamic programming, but differs in two main ways. YAGA applies the phase slope function on the wavelet transform of the source signal in order to emphasize the discontinuities in GCIs and GOIs.…”