# Evaluation of Glottal Inverse Filtering Techniques on
## 5 Discussion  
Some of the general trends observed from the results of this study can be linked with other similar studies found in the literature. For example, the better performance of QCP compared to the original IAIF approach in terms of NAQ, H1H2, and HRF was also found in the work of [PERSON] et al. in [27; 28]. However, it is worth mentioning that the error measures obtained in their study and the ones presented in this paper are not comparable. [PERSON] et al. used a synthetic vowel database constructed via the physical modeling of human speech production with different characteristics (e.g., the F0 range), and they fixed the GIF parameters, such as the vocal tract order, glottal source order, and lip radiation coefficient to fixed values for the set of analysed speech signals. Additionally, in the study by [PERSON] et al. [30], the authors found that methods based on weighted linear prediction, such as QCP approaches (denoted as sparse linear prediction and weighted linear prediction) obtained better performance with regard to IAIF using physically modelled synthetic sustained vowels with different ranges of physical parameter variations (e.g., F0, subglottal pressures, and vowel types). Furthermore, in the present work, it has been observed that there is a clear relationship between specific error measures and F0, as well as phonation type. In regard to F0, for higher F0 values, higher RMS and H1H2 errors are also obtained, as in the work of [PERSON] and [PERSON] [25], but the set of IAIF-based approaches obtain different patterns than our study (e.g., our results for the GFM-IAIF approach do not present with more stable behavior, nor is the technique with better performance across the entire set of signals). Concerning phonation type, our results mark a clear decrease in performance in terms of the RMS for tenser phonations (e.g., creaky). The same trend was observed for IOP-IAIF and GFM-IAIF by [PERSON] and [PERSON] in [25] for low \(R_{d}\) values (a parameter that correlates with phonation type). Furthermore, in terms of HRF and ST errors, these two techniques also respond worse for tenser phonations, while IAIF obtains better performance, as in [25]. However, it must be noticed that the four simulated phonation types along the OPENGLOT Repository I do not represent specific values of this \(R_{d}\) parameter; additionally, in the RMS error computation procedure reported in [25], the mean and scale normalization process were not specified. As a global trend, it can be noticed that QCP-based methods outperform their IAIF-based counterparts. However, there are still some specific situations where IAIF-based techniques obtain better results, e.g., in terms of HRF and ST for low F0 ranges or NAQ for whisper phonation (in this last case, both the original IAIF and GFM-IAIF obtain the distribution with the lowest values together with QCP). Nevertheless, it is worth mentioning that QCP-based methods use extra information with respect to IAIF-based ones due to the closing phase timings that are provided by the CGIs, which, in turn, are used as input to define the AME function. This fact could partially explain the better performance observed in QCP-based methods, together with the fact that CGIs are also used for glottal error measure computation. However, when working on real speech data, the CGIs may be difficult to extract, thus affecting the performance of the QCP-based approaches negatively. This is a relevant issue that should be studied in future works. In the present work, an extension of OPENGLOT Repository I was generated to include vowels produced with female formant vocal tracts. By maintaining the same ranges of exploration for the other signal attributes, like F0, vowel, or phonation type, we can study the influence of the formants in the inverse filtering results. However, it must be appreciated that sex-related dependencies in other signal attributes, such as the F0 vocal range, were not included in the study.