## 4 Discussion
### Method of RFF Computation  
Prior work on RFF indicates that approximately 20-30 min of manual processing time is necessary to calculate a reliable estimate for a single subject. Numerous studies (e.g., [51]) discuss the clinical implications of such an extensive processing time (not to mention the considerable time required to train oneself to manually estimate RFF), which point to a need for faster, automated methodologies for RFF to be implemented in voice clinics. Additional review of the literature indicated that there was, indeed, an increase in algorithmic investigations and implementation of these methodologies since their availability in 2016; 9 of the 17 studies (53%) published since 2016 have utilized algorithmic extraction, indicating that algorithmic adoption is a viable option for many researchers. The aRFF algorithm [39] is the most commonly used automated method to date. This algorithm, much like software used during manual estimation (e.g., Praat), uses autocorrelation to determine cycle \(f_{\rm o}\). It follows that one would expect a close correspondence between automated and manual estimates. [PERSON] et al. [39] reported a strong relationship (\(r\geq 0.82\)) between aRFF and manual estimates for offset and onset values; however, the authors also found that the aRFF algorithm consistently estimates higher offset cycle 10 values (+0.22 to +0.41 ST) and lower onset cycle 1 values (\(-0.11\) to \(-0.10\) ST) when compared to manual estimates. Although these systematic errors are smaller than the increase in RFF that can be expected after undergoing voice therapy [24] (indicating that, on average, clinically meaningful changes in RFF will not be masked by errors associated with using the RFF algorithm), it is still important to consider the source of algorithm errors. One reason for this discrepancy may be that manual estimates allow the user to visually identify voicing offset and onset, whereas the algorithm must use computational techniques for vowel-fricative boundary identification. Boundary cycle shifts are most likely to impact the cycle values closest to the fricative (i.e., offset cycle 10 and onset cycle 1) and require refinement for consistent value interpretation and clinical adoption. Each new algorithm iteration has improved upon the last by increasing the accuracy of cycle detection through various signal processing considerations. For example, [PERSON] et al. [51] incorporated new pitch estimation methods in aRFF-AP to more closely align algorithm extracted RFF values with manually acquired values. Within this algorithm, the authors also introduced a set of signal-specific parameters to account for differences in voice sample characteristics (e.g., overall severity of dysphonia, signal acquisition quality) based on the acoustic measure, pitch strength [55; 56]. In doing so, errors affecting the accuracy of the RFF algorithm were reduced by 88.4% relative to manually acquired values. Further exploration into improving the accuracy of algorithmically extracted RFF values has yielded a new set of acoustic feature processing techniques to locate the voicing cycles closest to the voiceless consonant (i.e., offset cycle 10, onset cycle 1). This most recent version of the RFF algorithm (aRFF-APH) [52] was built by integrating information from simultaneous recordings made using a microphone and high-speed flexible laryngoscopy. The authors were able to identify the physiological--rather than acoustic--initiation and termination of vocal fold vibration that marks the boundaries of the voiceless consonant. Interestingly, the study reported that both algorithm iterations (aRFF-AP, aRFF-APH) were significantly more likely to identify offset cycle 10 and onset cycle 1 values that coincided with physiologically identified vocal fold vibratory timings than manually acquired values, with aRFF-AP having the strongest correspondence with physiologic events. The results of this work call into question the use of manual RFF as a \"ground truth\" comparison for algorithmic testing.