## 4 Discussion
### Manual RFF Estimation as a Gold Standard  
Despite the aforementioned differences between the aRFF-AP and aRFF-APH algorithms, using either of these methods resulted in a greater boundary cycle identification accuracy than when using manual estimation. These findings are unexpected since manual estimation has long been considered the gold-standard RFF estimation method. Specifically, manual estimation has been long considered the benchmark for RFF since trained technicians may exercise trial and error to identify the boundary cycle in difficult scenarios (e.g., poor recording environment and/or equipment, severe dysphonia) when cycle masking is present. Prior published work has not compared microphone-derived estimates of RFF between manual and algorithmic estimation beyond that of [PERSON] et al. (2017) and [PERSON] et al. (2014), which tuned the semi-automated RFF algorithm to manual estimates. Moreover, prior work has not compared acoustically derived intervocalic voicing offsets of RFF stimuli to those identified via high-speed videoendoscopy. These findings call into question the utility of manual RFF estimation as a benchmark for accuracy comparisons. It is possible that the characteristics of the speaker database confounded this outcome, as noise from the endoscopic light source may have masked the voice signals and/or speaker productions may have deviated from the norm due to the flexible anastomoc. Even though manual estimation makes use of trial and error to subjectively locate the boundary cycle when masking is present, it is possible that manual estimation techniques were not sensitive enough to isolate the physiological boundary cycle in these conditions. On the other hand, the aRFF-AP algorithm was designed to account for such variationsand the aRFF-APH algorithm was refined based on the physiologically determined vocal fold characteristics. Both algorithms also identify potential vocal cycles using a filtered version of the microphone signal that was designed to reduce the amplitude of vocal tract resonances, coarticulation due to concurrent frication and aspiration, and radiation of the lips. By only using the raw microphone signal to identify vocal cycles, the RFF values resulting from manual estimation may not reflect the true offset or onset of voicing as expected. Although manual estimation resulted in the lowest boundary cycle identification accuracy, it is important to note that most misclassifications occurred within two pitch periods of the true boundary cycle for both voicing offset and onset (see Figure 5). These findings are similar to those of [PERSON] et al. [50], in which manual RFF estimation was compared when performed on a microphone signal versus a neck-surface accelerometer signal. Since a neck-surface accelerometer is able to capture the vibrations of the glottal source in the absence of vocal cycle masking due to frication and aspiration (as may occur during the production of an intervocalic fricative; [27]), the accelerometer signal was considered to be a ground truth over the microphone signal. The authors observed that misclassifications occurred closer to the vowel for both voicing offset and onset when performing manual RFF estimation using a microphone signal rather than an accelerometer signal. Whereas offset RFF values were extracted approximately two cycles closer to the vowel when using a microphone signal, onset RFF values were computed less than one cycle away from the voiceless consonant when using a microphone signal. The results of the current study support these findings and, moreover, lend support to the supposition that the aRFF-AP and aRFF-APH algorithms benefit from using a band-pass filtered version of the microphone signal to identify potential vocal cycles. As semi-automated RFF algorithm accuracy is typically quantified in reference to manual RFF estimation (e.g., see [22; 24]), it is important to consider that manual estimation may not be a true gold-standard technique.