# Evaluation of Glottal Inverse Filtering Techniques on
## 1 Introduction
[22] offers an alternative approach to estimate the GS and VT transfer functions based on classic linear prediction coding (LPC). This algorithm employs a two-step iterative process that involves an initial gross estimation followed by a refined estimation of GS and VT transfer functions. A subsequent work by [PERSON] [23] included high-pass filtering (HPF) and pitch-synchronous analysis, showing potential improvements, though the results were only analysed qualitatively. More recently, several variants of the IAIF algorithm have been developed and documented in the literature. One such variant, known as iterative optimal pre-emphasis (IOP-IAIF), replaces the initial gross step of IAIF with an iterative pre-emphasis approach [24]. Additionally, a modified version called the glottal flow model (GFM)-IAIF restricts the GS filter order in both the initial gross estimation and the refined stages [25]. Within the third category of GIF techniques, we can find those approaches supported by the estimation of the glottal closing and/or opening instants. Closed-phase covariance analysis uses both estimations to derive an all-pole VT transfer function from speech samples within the closed-phase time region, to this way estimate the glottal flow signal [26]. However, as [PERSON] et al. state in their seminal paper [26], this technique relies on estimations of glottal closure instants (GCIs) and glottal opening instants, being the latter less reliable than the former. Focusing only on the estimation of GCIs, thus simplifying the process, the quasi-closed phase method (QCP) [27] performs VT estimation based on a weighting function that, while uses the whole set of signal samples, emphasizes on the speech samples within the closed-phase region [27; 28]. As a potential method to optimize the QCP method's performance, [PERSON] et al. in [29] appended a spectral tilt compensation module as a post-processing step to minimize the residual spectral cues from the glottal source (hereafter denoted as ST-QCP). The performance evaluation of GIF methods typically involves considering diverse error measures; the most commonly employed are [17; 25; 30; 31] the root-mean-square (RMS), normalized amplitude quotient (NAQ), quasi-open quotient (QOQ), harmonic richness factor (HRF), H1H2 (relationship between the first and second harmonics), spectral distortion, parabolic spectrum parameter, and spectral tilt, among others. These measures serve as quantitative indicators of the accuracy of the estimated glottal source signal after applying a specific GIF technique. Despite considering common error metrics, these evaluations are typically conducted using either real or synthetic speech data generated by the authors themselves (see, e.g., [17; 25; 30; 31]). Therefore, the reliable analysis of the results reported in the literature becomes quite challenging, making the direct comparison between GIF techniques, at least, intricate, or even unfeasible due to the high dependency of the specific experimental settings considered in each study. Table 11 presents the main characteristics of the speech databases used for GIF evaluation in previous literature, listed in terms of pitch range, vowels, type of dataset (synthetic or real speech), sample frequency, and phonation type. To our knowledge, some of the listed databases are not publicly available, which limits the ability to draw comparisons with other methods with exactly the same conditions. To tackle this problem, the recent publication of the OPENGLOT [32] dataset becomes instrumental, especially since it has been specifically designed by researchers from this research field to assess different GIF methods on the same common reference environment. The main pillars of OPENGLOT are twofold: first, it provides a representative variety of test signals for GIF evaluation and, second, it is an open dataset; thus, it allows the evaluation and comparison of any GIF method with respect to previously developed benchmark techniques.